Methods for monitoring structural health conditions

ABSTRACT

Methods and recordable media for monitoring structural health conditions. The present invention provides a method for interrogating a damage of a host structure using a diagnostic network patch (DNP) system having patches. An interrogation module partitions the plurality of patched in subgroups and measures the sensor signals generated and received by actuator and sensor patches, respectively. Then, a process module loads sensor signal data to identify Lamb wave modes, determine the time of arrival of the modes and generate a tomographic image. It also determines distribution of other structural condition indices to generate tomographic images of the host structure. A set of tomographic images can be stacked to generate a hyperspectral tomography cube. A classification module generates codebook based on K-mean/Learning Vector Quantization algorithm and uses a neural-fuzzy-inference system to determine the type of damages of the host structure.

CROSS REFERENCE TO RELATED APPLICTIONS

This application claims the benefit of U.S. Provisional Applications No.60/505,120, entitled “sensor and system for structural healthmonitoring,” filed on Sep. 22, 2003, which is hereby incorporated hereinby reference in its entirety.

FIELD OF INVENTION

The present invention relates to diagnostics of structures, and moreparticularly to methods for monitoring structural health conditions.

BACKGROUND OF THE INVENTION

As all structures in service require appropriate inspection andmaintenance, they should be monitored for their integrity and healthcondition to prolong their life or to prevent catastrophic failure.Apparently, the structural health monitoring has become an importanttopic in recent years. Numerous methods have been employed to identifyfault or damage of structures, where these methods may includeconventional visual inspection and non-destructive techniques, such asultrasonic and eddy current scanning, acoustic emission and X-rayinspection. These conventional methods require at least temporaryremoval of structures from service for inspection. Although still usedfor inspection of isolated locations, they are time-consuming andexpensive.

With the advance of sensor technologies, new diagnostic techniques forin-situ structural integrity monitoring have been in significantprogress. Typically, these new techniques utilize sensory systems ofappropriate sensors and actuators built in host structures. However,these approaches have drawbacks and may not provide effective on-linemethods to implement a reliable sensory network system and/or accuratemonitoring methods that can diagnose, classify and forecast structuralcondition with the minimum intervention of human operators. For example,U.S. Pat. No. 5,814,729, issued to Wu et al., discloses a method thatdetects the changes of damping characteristics of vibrational waves in alaminated composite structure to locate delaminated regions in thestructure. Piezoceramic devices are applied as actuators to generate thevibrational waves and fiber optic cables with different gratinglocations are used as sensors to catch the wave signals. A drawback ofthis system is that it cannot accommodate a large number of actuatorarrays and, as a consequence, each of actuators and sensors must beplaced individually. Since the damage detection is based on the changesof vibrational waves traveling along the line-of-sight paths between theactuators and sensors, this method fails to detect the damage locatedout of the paths and/or around the boundary of the structure.

Another approach for damage detection can be found in U.S. Pat. No.5,184,516, issued to Blazic et al., that discloses a self-containedconformal circuit for structural health monitoring and assessment. Thisconformal circuit consists of a series of stacked layers and traces ofstrain sensors, where each sensor measures strain changes at itscorresponding location to identify the defect of a conformal structure.The conformal circuit is a passive system, i.e., it does not have anyactuator for generating signals. A similar passive sensory networksystem can be found in U.S. Pat. No. 6,399,939, issued to Mannur, J. etal. In Mannur '939 patent, a piezoceramic-fiber sensory system isdisclosed having planner fibers embedded in a composite structure. Adrawback of these passive methods is that they cannot monitor internaldelamination and damages between the sensors. Moreover, these methodscan detect the conditions of their host structures only in the localareas where the self-contained circuit and the piezoceramic-fiber areaffixed.

One method for detecting damages in a structure is taught by U.S. Pat.No. 6,370,964 (Chang et al.). Chang et al. discloses a sensory networklayer, called Stanford Multi-Actuator-Receiver Transduction (SMART)Layer. The SMART Layer® includes piezoceramic sensors/actuatorsequidistantly placed and cured with flexible dielectric filmssandwiching the piezoceramic sensors/actuators (or, shortly,piezoceramics). The actuators generate acoustic waves and sensorsreceive/transform the acoustic waves into electric signals. To connectthe piezoceramics to an electronic box, metallic clad wires are etchedusing the conventional flexible circuitry technique and laminatedbetween the substrates. As a consequence, a considerable amount of theflexible substrate area is needed to cover the clad wire regions. Inaddition, the SMART Layer® needs to be cured with its host structuremade of laminated composite layers. Due to the internal stress caused bya high temperature cycle during the curing process, the piezoceramics inthe SMART Layer® can be micro-fractured. Also, the substrate of theSMART Layer® can be easily separated from the host structure. Moreover,it is very difficult to insert or attach the SMART Layer® to its hoststructure having a curved section and, as a consequence, a compressiveload applied to the curved section can easily fold the clad wires.Fractured piezoceramics and the folded wires may be susceptible toelectromagnetic interference noise and provide misleading electricalsignals. In harsh environments, such as thermal stress, field shock andvibration, the SMART Layer® may not be a robust and unreliable tool formonitoring structural health. Furthermore, the replacement of damagedand/or defective actuators/sensors may be costly as the host structureneeds to be dismantled.

Another method for detecting damages in a structure is taught by U.S.Pat. No. 6,396,262 (Light et al.). Light et al. discloses amagnetostrictive sensor for inspecting structural damages, where thesensor includes a ferromagnetic strip and a coil closely located to thestrip. The major drawback of this system is that the system cannot bedesigned to accommodate an array of sensors and, consequently, cannotdetect internal damages located between sensors.

Due to the mentioned drawbacks, the methodologies for analyzing datathat are implemented in these conventional systems may have limitationsin monitoring the host structures in an accurate and efficient manner.Thus, there is a need for new and efficient methodologies for analyzingand interpreting the data from the host systems to determine structuralconditions and to prognosticate failures.

OBJECTS AND ADVANTAGES

Accordingly, it is one object of the invention to provide an accuratetechnique for determining the structural condition by using differenttypes of methods, such as bisection, intersection, andadaptive-neural-fuzzy-inference positioning of network paths, where thetechnique is incorporated with convex-set interpolation.

It is another object of the invention to provide a reliable techniquefor determining the structural condition by integrating the computedtomography algorithms for different structural condition indices.

It is yet another object of the invention to provide a method forinterpreting the structural condition by the use of a hyperspectraltomography cube and a structural condition manifold.

It is still another object of the invention to provide a technique forclassifying the structural condition by the use of a codebook-templatebased classifier, where the technique is incorporated with themultilayer perception on the tomography of a structure.

It is a further object of the invention to provide a prognostictechnique for forecasting structural condition by modeling thediagnostic network system and updating its parameters, where thetechnique is incorporated with system identification and a supervisedlearning algorithm.

SUMMARY OF THE INVENTION

These and other objects and advantages are attained by a structuralhealth monitoring software that comprises interrogation, processing,classification and prognosis modules and analyses data from a diagnosticnetwork patch (DNP) system that is attached to a host composite and/ormetallic structure. The DNP system contains actuators/sensors andprovides an internal wave-ray communication network in the hoststructure by transmitting acoustic wave impulses (or, equivalently, Lambwaves) between the actuators/sensors.

According to one aspect of the present invention, a method forinterrogating damages, identifying impacts and monitoring curing andrepaired-boning-patch performance of a composite structure using adiagnostic network patch (DNP) system that is implemented thereto andcomprises a plurality of patches, includes steps of: partitioning theplurality of patches into one or more subgroups, each of the one or moresubgroups having at least one actuator patch and at least one sensorpatch; designing a network and a plurality of signal paths using agenetic algorithm; generating a signal by activating a first one of theplurality of patches; receiving the generated signal via a second one ofthe plurality of patches through a corresponding one of the plurality ofsignal paths; comparing the received signal with a baseline signal tointerrogate the damage, the baseline signal measured in absence of thedamages; and storing the received signal and deviation of the receivedsignal from the baseline signal.

According to another aspect of the present invention, a method foridentifying Lamb wave modes and determining time of arrivals of the Lambwave modes includes steps of: loading a set of sensor signal data, eachsensor signal data comprising Lamb wave signals measured at one ofpredetermined excitation frequencies; detrending each of the set ofsensor signal data to remove non-stationary signal component; removingan electrical noise due to a toneburst actuator signal by applying amasking window to the each detrended sensor signal data; performing atransformation on the each noise-removed sensor signal data to obtain atime-frequency signal energy distribution; generating a multi-bandwidthenergy distribution on a time-frequency plane by accumulating entire setof time-frequency signal energy distributions; extracting one or moreridges from the multi-bandwidth energy distribution; and identifyingLamb wave modes and determining time of arrivals of the Lamb wave modesbased on the extracted one or more ridges.

According to still another aspect of the present invention, a method forgenerating structural condition index (SCI) datasets from a plurality ofsensor signal datasets includes steps of: (a) loading a plurality ofsensor signal datasets for a plurality of network paths, each of theplurality of sensor signal datasets measured at one excitationfrequency; (b) selecting one of the plurality of sensor signal datasets;(c) selecting one sensor signal from the selected sensor signal dataset,wherein the selected sensor signal is a Lamb wave signal; (d) detrendand partition the selected sensor signal by applying an average filterand a masking window, respectively; (e) decomposing the partitionedsensor signal into sub-bandwidth wave packets by applying a waveletdecomposition filter; (f) synthesizing new sub-bandwidth packets; (g)extracting S₀, S₀ _(—) _(ref), and A₀ modes from the synthesizedsub-bandwidth packets by applying a set of envelop windows; (h)computing at least one parameter of the set of envelop windows; (i)determining a structural condition index for the selected sensor signal;(j) determining a first discrete probability distribution function(DPDF) of the selected sensor signal; (k) calculating a normalityconstant of the selected sensor signal; (l) repeating the steps (d)-(k)for each sensor signal of the selected sensor signal dataset; (m)determining a second DPDF for SCI dataset comprising structuralcondition indices obtained at step (i); (n) finding and removing one ormore outliers of the second DPDF; (o) compensating an effect of ambienttemperature on the SCI dataset; (p) storing the SCI dataset; and (q)repeating the steps (c)-(p) for each of the plurality of sensor signaldatasets.

According to yet another aspect of the present invention, a method forgenerating a tomographic image to identify a region having changes instructural conditions that include damages of a host structure includessteps of: (a) loading a coordinate data of a plurality of diagnosticpatches and a set of structural condition index (SCI) values for networkpaths defined by the plurality of diagnostic patches, the set of SCIvalues measured at an excitation frequency; (b) calculating a bisectionpoint for each of the network paths and assigning a corresponding one ofthe set of SCI values to the bisection point; (c) calculatingintersection points of the network paths; (d) designating a SCI productto each of the intersection points; (e) calculating SCI values near theintersection points using 3-dimensional SCI Gaussian functions, each ofthe 3-dimensional Gaussian functions defined for each of the networkpaths; (f) generating a SCI distribution over a network plane byinterpolation and a set of mesh-grid points of the network plane; (g)setting up a chromosome population by assigning each chromosome to acorresponding one of the mesh-grid points; (h) evaluating and rankingthe chromosome population; (i) selecting parent chromosomes from thechromosome population and reproducing child chromosomes; (j) replacingthe parent chromosomes with the reproduced child chromosomes; (k)repeating the steps (i)-(j) over a preset number of generations togenerate a final population of chromosomes; (l) refining the SCIdistribution over the final population of chromosomes; and (m)generating a tomographic image of the refined SCI distribution.

According to a further aspect of the present invention, a method forgenerating a tomographic image to identify changes in structuralconditions or damages of a host structure includes steps of: loading atime of arrival dataset of a Lamb wave mode for a plurality of networkpaths defined by a plurality of diagnostic patches, the plurality ofdiagnostic patches applied to the host structure; applying an algebraicreconstruction technique to reconstruct the loaded time of arrivaldataset; and generating a tomographic image of entire region of the hoststructure based on the reconstructed dataset.

According to a still further aspect of the present invention, a methodfor developing a codebook that is utilized to classify types of damagesin a structure includes steps of: (a) initializing a set of clustercenters by randomly selecting a plurality of structural condition index(SCI) values on a plurality of grid points; (b) determining a membershipmatrix; (c) compute a cost function; (d) updating the set of clustercenters; (e) repeating the steps (b)-(d) if the cost is greater than atolerance and the cost decreases upon repetition of the steps (b)-(d);(f) labeling the set of cluster enters by a voting method; (g) selectinga training SCI input vector randomly and choosing one of the clustercenters that is closest to the training SCI input vector; (h) updatingthe chosen cluster center if the SCI input vector and the chosen clustercenter belong to a same class; and (i) generating a codebook includingthe updated cluster center.

According to another aspect of the present invention, a method forgenerating a 3-dimensional damage evolution manifold includes steps of:providing a host structure; making a set of 2-dimensional tomographicimages, each of the set of tomographic images generated after acorresponding number of vibrational repetitions are applied to the hoststructure; and stacking the tomographic images in an increasing order ofvibrational repetitions to generate a 3-dmensional damage evolutionmanifold.

According to anther aspect of the present invention, a method fordeveloping a prognosis model to forecast damage evolution in a structureincludes steps of: (a) building an input-output system model for aLamb-wave network system having at least one actuator and at least onesensor at a selected time step; (b) identifying the input-output systemmodel using a state space system identification method; (c) training aprevious input-output system model with SCI values to generate anone-step-ahead system model using a recurrent neural network, the SCIvalues provided from an input-output system model built at a previoustime step; (d) generating output signals from the one-step-ahead systemmodel using input signals measured by the at least one sensor; (e)computing SCI values from the output signals; (f) repeating the steps(c)-(e) until the iteration steps reaches a preset time of forecastingdamage of the structure; (g) generating future output signals of afuture system model using the input signals, wherein the future systemmodel is an input-output system model built at the preset time offorecasting damage; and (h) providing SCI values of the future outputsignals.

These and other advantages and features of the invention will becomeapparent to those persons skilled in the art upon reading the details ofthe invention as more fully described below.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing will be provided by the Office upon request and payment ofthe necessary fee.

FIG. 1A is a schematic top cut-away view of a patch sensor in accordancewith one embodiment of the present teachings.

FIG. 1B is a schematic side cross-sectional view of the patch sensorshown in FIG. 1A.

FIG. 1C is a schematic top view of a typical piezoelectric device thatmay be used in the patch sensor of FIG. 1A.

FIG. 1D is a schematic side cross-sectional view of the typicalpiezoelectric device in FIG. 1C.

FIG. 1E is a schematic top cut-away view of a patch sensor in accordancewith another embodiment of the present teachings.

FIG. 1F is a schematic side cross-sectional view of the patch sensorshown in FIG. 1E.

FIG. 1G is a schematic cross-sectional view of a composite laminateincluding the patch sensor of FIG. 1E.

FIG. 1H is a schematic side cross-sectional view of an alternativeembodiment of the patch sensor of FIG. 1E.

FIG. 2A is a schematic top cut-away view of a hybrid patch sensor inaccordance with one embodiment of the present teachings.

FIG. 2B is a schematic side cross-sectional view of the hybrid patchsensor shown in FIG. 2A.

FIG. 2C is a schematic top cut-away view of a hybrid patch sensor inaccordance with another embodiment of the present teachings.

FIG. 2D is a schematic side cross-sectional view of the hybrid patchsensor shown in FIG. 2C.

FIG. 3A is a schematic top cut-away view of an optical fiber patchsensor in accordance with one embodiment of the present teachings.

FIG. 3B is a schematic side cross-sectional view of the optical fiberpatch sensor shown in FIG. 3A.

FIG. 3C is a schematic top cut-away view of the optical fiber coilcontained in the optical fiber patch sensor of FIG. 3A.

FIG. 3D is a schematic top cut-away view of an alternative embodiment ofthe optical fiber coil shown in FIG. 3C.

FIGS. 3E-F are schematic top cut-away views of alternative embodimentsof the optical fiber coil of FIG. 3C.

FIG. 3G is a schematic side cross-sectional view of the optical fibercoil of FIG. 3E.

FIG. 4A is a schematic top cut-away view of a diagnostic patch washer inaccordance with one embodiment of the present teachings.

FIG. 4B is a schematic side cross-sectional view of the diagnostic patchwasher shown in FIG. 4A.

FIG. 4C is a schematic diagram of an exemplary bolt-jointed structureusing the diagnostic patch washer of FIG. 4A in accordance with oneembodiment of the present teachings.

FIG. 4D is a schematic diagram of an exemplary bolt-jointed structureusing the diagnostic patch washer of FIG. 4A in accordance with anotherembodiment of the present teachings.

FIG. 5A is a schematic diagram of an interrogation system including asensor/actuator device in accordance with one embodiment of the presentteachings.

FIG. 5B is a schematic diagram of an interrogation system including asensor in accordance with one embodiment of the present teachings.

FIG. 6A is a schematic diagram of a diagnostic network patch systemapplied to a host structure in accordance with one embodiment of thepresent teachings.

FIG. 6B is a schematic diagram of a diagnostic network patch systemhaving a strip network configuration in accordance with one embodimentof the present teachings.

FIG. 6C is a schematic diagram of a diagnostic network patch systemhaving a pentagon network configuration in accordance with oneembodiment of the present teachings.

FIG. 6D is a schematic perspective view of a diagnostic network patchsystem incorporated into rivet/bolt-jointed composite laminates inaccordance with one embodiment of the present teachings.

FIG. 6E is a schematic perspective view of a diagnostic network patchsystem incorporated into a composite laminate repaired with a bondingpatch in accordance with another embodiment of the present teachings.

FIG. 6F is a schematic diagram illustrating an embodiment of a wirelesscommunication system that controls a remote diagnostic network patchsystem in accordance with one embodiment of the present teachings.

FIG. 7A is a schematic diagram of a diagnostic network patch systemhaving clustered sensors in a strip network configuration in accordancewith one embodiment of the present teachings.

FIG. 7B is a schematic diagram of a diagnostic network patch systemhaving clustered sensors in a pentagonal network configuration inaccordance with another embodiment of the present teachings.

FIG. 8A is a schematic diagram of a clustered sensor having opticalfiber coils in a serial connection in accordance with one embodiment ofthe present teachings.

FIG. 8B is a schematic diagram of a clustered sensor having opticalfiber coils in a parallel connection in accordance with anotherembodiment of the present teachings.

FIG. 9 is a plot of actuator and sensor signals in accordance with oneembodiment of the present teachings.

FIG. 10 is a flow chart illustrating exemplary procedures of aninterrogation module in accordance with one embodiment of the presentteachings.

FIG. 11A is a schematic diagram of an exemplary actuator networkarchitecture including subgroups in accordance with one embodiment ofthe present teachings.

FIG. 11B is a schematic diagram of a network architecture havingactuators/sensors subgroups in accordance with another embodiment of thepresent teachings.

FIG. 12 is a flow chart illustrating exemplary procedures foridentifying Lamb wave modes in accordance with one embodiment of thepresent teachings.

FIGS. 13A-B show a flow chart illustrating exemplary procedures forcomputing SCI values in accordance with one embodiment of the presentteachings.

FIG. 14A is a flow chart illustrating exemplary procedures forgenerating a tomographic image to identify the regions having changes instructural conditions or damages in accordance with one embodiment ofthe present teachings.

FIG. 14B is a flow chart illustrating exemplary procedures forgenerating a tomographic image to identify the regions having changes instructural conditions or damages in accordance with another embodimentof the present teachings.

FIG. 14C is a tomography image generated by the procedures of FIG. 14A.

FIG. 14D shows a hyperspectral tomography cube in accordance with oneembodiment of the present teachings.

FIG. 14E shows a 3-dimensional damage evolution manifold illustratingthe variation of structural condition in accordance with one embodimentof the invention.

FIG. 15A is a schematic diagram illustrating exemplary procedures of aneuro-fuzzy inference system for providing structured system conditionindex (SCI) distribution at the intersection points of network paths inaccordance with one embodiment of the invention.

FIG. 15B is a schematic diagram illustrating exemplary procedures of acooperative hybrid expert system for simulating SCI distribution on thelattice grid points of a structure in accordance with one embodiment ofthe invention.

FIG. 16A is a schematic diagram illustrating Gabor jets applied to a‘hot-spot’ region in accordance with one embodiment of the presentteachings.

FIG. 16B is a schematic diagram illustrating multilayer perception (MLP)for classifying the types of damages in accordance with one embodimentof the present teachings.

FIG. 16C is a schematic diagram illustrating a fully-connected networkclassifier for classifying a structural condition in accordance with oneembodiment of the present teachings.

FIG. 16D is a schematic diagram illustrating modular network classifiersfor classifying structural conditions in accordance with one embodimentof the present teachings.

FIG. 17A is a flow chart illustrating exemplary procedures of aK-mean/learning vector quantization (LVQ) algorithm for developing acodebook in accordance with one embodiment of the present teachings.

FIG. 17B is a schematic diagram illustrating exemplary procedures of aclassification module to build a damage classifier using a codebookgenerated by the steps in FIG. 17A in accordance with one embodiment ofthe present teachings.

FIG. 18A is a schematic diagram illustrating three evolution domains ofa structure in operation/service, dynamics of sensory network system,and network system matrix, according to one embodiment of the invention.

FIG. 18B schematically represents the architecture of a recurrent neuralnetwork for forecasting the future system matrix in accordance with oneembodiment of the present teachings.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Although the following detained description contains many specifics forthe purposes of illustration, those of ordinary skill in the art willappreciate that many variations and alterations to the following detainsare within the scope of the invention. Accordingly, the followingembodiments of the invention are set forth without any loss ofgenerality to, and without imposing limitation upon, the claimedinvention.

The publications discussed herein are provided solely for theirdisclosure prior to the filing date of the present application. Nothingherein is to be construed as an admission that the present invention isnot entitled to antedate such publication by virtue of prior invention.Further, the dates of publication provided may be different from theactual publication dates, which may need to be independently confirmed.

FIG. 1A is a schematic top cut-away view of a patch sensor 100 inaccordance with one embodiment of the present teachings. FIG. 1B is aschematic cross-sectional view of the patch sensor 100 taken along adirection A-A of FIG. 1A. As shown in FIGS. 1A-B, the patch sensor 100may include: a substrate 102 configured to attach to a host structure; ahoop layer 104; a piezoelectric device 108 for generating and/orreceiving signals (more specifically, Lamb waves); a buffer layer 110for providing mechanical impedance matching and reducing thermal stressmismatch between the substrate 102 and the piezoelectric device 108; twoelectrical wires 118 a-b connected to the piezoelectric device 108; amolding layer 120 for securing the piezoelectric device 108 to thesubstrate 102; and a cover layer 106 for protecting and sealing themolding layer 120. The piezoelectric device 108 includes: apiezoelectric layer 116; a bottom conductive flake 112 connected to theelectrical wire 118 b; and a top conductive flake 114 connected to theelectrical wire 118 a. The piezoelectric device 108 may operate as anactuator (or, equivalently, signal generator) when a pre-designedelectric signal is applied through the electric wires 118 a-b. Uponapplication of an electrical signal, the piezoelectric layer 116 maydeform to generate Lamb waves. Also, the piezoelectric device 108 mayoperate as a receiver for sensing vibrational signals, converting thevibrational signals applied to the piezoelectric layer 116 into electricsignals and transmitting the electric signals through the wires 118 a-b.The wires 118 a-b may be a thin ribbon type metallic wire.

The substrate 102 may be attached to a host structure using a structuraladhesive, typically a cast thermosetting epoxy, such as butyralthenolic,acrylic polyimide, nitriale phenolic or aramide. The substrate 102 maybe an insulation layer for thermal heat and electromagnetic interferenceprotecting the piezoelectric device 108 affixed to it. In someapplications, the dielectric substrate 102 may need to cope with atemperature above 250° C. Also it may have a low dielectric constant tominimize signal propagation delay, interconnection capacitance andcrosstalk between the piezoelectric device 108 and its host structure,and high impedance to reduce power loss at high frequency.

The substrate 102 may be made of various materials. Kapton® polyimidemanufactured by DuPont, Wilmington, Del., may be preferably used for itscommonplace while other three materials of Teflon perfluoroalkoxy (PFA),poly p-xylylene (PPX), and polybenzimidazole (PBI), can be used fortheir specific applications. For example, PFA film may have gooddielectric properties and low dielectric loss to be suitable for lowvoltage and high temperature applications. PPX and PBI may providestable dielectric strength at high temperatures.

The piezoelectric layer 116 can be made of piezoelectric ceramics,crystals or polymers. A piezoelectric crystal, such as PZN-PT crystalmanufactured by TRS Ceramics, Inc., State College, Pa., may bepreferably employed in the design of the piezoelectric device 108 due toits high strain energy density and low strain hysteresis. For small sizepatch sensors, the piezoelectric ceramics, such as PZT ceramicsmanufactured by Fuji Ceramic Corporation, Tokyo, Japan, or APCInternational, Ltd., Mackeyville, Pa., may be used for the piezoelectriclayer 116. The top and bottom conductive flakes 112 and 114 may be madeof metallic material, such as Cr or Au, and applied to the piezoelectriclayer 116 by the conventional sputtering process. In FIG. 1B, thepiezoelectric device 108 is shown to have only a pair of conductiveflakes. However, it should be apparent to those of ordinary skill thatthe piezoelectric device 108 may have the multiple stacks of conductiveflakes having various thicknesses to optimize the performance of thepiezoelectric layer 116 in generating/detecting signal waves. Thethickness of each flake may be determined by the constraints of thermaland mechanical loads given in a particular host structure that the patchsensor 100 is attached to.

To sustain temperature cycling, each layer of the piezoelectric device108 may need to have a thermal expansion coefficient similar to those ofother layers. Yet, the coefficient of a typical polyimide comprising thesubstrate 102 may be about 4-6×10⁻⁵ K⁻¹ while that of a typicalpiezoelectric ceramic/crystal comprising the piezoelectric layer 116 maybe about 3×10⁻⁶K⁻¹. Such thermal expansion mismatch may be a majorsource of failure of the piezoelectric device 108. The failure ofpiezoelectric device 108 may require a replacement of the patch sensor100 from its host structure. As mentioned, the buffer layer 110 may beused to reduce the negative effect of the thermal coefficient mismatchbetween the piezoelectric layer 116 and the substrate 102.

The buffer layer 110 may be made of conductive polymer or metal,preferably aluminum (Al) with the thermal expansion coefficient of2×10⁻⁵K⁻¹. One or more buffer layers made of alumina, silicon orgraphite may replace or be added to the buffer layer 110. In oneembodiment, the thickness of the buffer layer 110 made of aluminum maybe nearly equal to that of the piezoeletric layer 116, which isapproximately 0.25 mm including the two conductive flakes 112 and 114 ofabout 0.05 mm each. In general, the thickness of the buffer layer 110may be determined by the material property and thickness of its adjacentlayers. The buffer layer 110 may provide an enhanced durability againstthermal loads and consistency in the twofold function of thepiezoelectric device 108. In an alternative embodiment, thepiezoelectric device 108 may have another buffer layer applied over thetop conductive flake 114.

Another function of the buffer layer 110 may be amplifying signalsreceived by the substrate 102. As Lamb wave signals generated by a patchsensor 100 propagate along a host structure, the intensity of thesignals received by another patch sensor 100 attached on the hoststructure may decrease as the distance between the two patch sensorsincreases. When a Lamb signal arrives at the location where a patchsensor 100 is located, the substrate 102 may receive the signal. Then,depending on the material and thickness of the buffer layer 110, theintensity of the received signal may be amplified at a specificfrequency. Subsequently, the piezoelectric device 108 may convert theamplified signal into electrical signal.

As moisture, mobile ions and hostile environmental condition may degradethe performance and reduce the lifetime of the patch sensor 100, twoprotective coating layers, a molding layer 120 and a cover layer 106 maybe used. The molding layer 120 may be made of epoxy, polyimide orsilicone-polyimide by the normal dispensing method. Also, the moldinglayer 120 may be formed of a low thermal expansion polyimide anddeposited over the piezoelectric device 108 and the substrate 102. Aspassivation of the molding layer 120 does not make a conformal hermeticseal, the cover layer 106 may be deposited on the molding layer 120 toprovide a hermitic seal. The cover layer 120 may be made of metal, suchas nickel (Ni), chromium (Cr) or silver (Ag), and deposited by aconventional method, such as electrolysis or e-beam evaporation andsputtering. In one embodiment, an additional film of epoxy or polyimidemay be coated on the cover layer 106 to provide a protective layeragainst scratching and cracks.

The hoop layer 104 may be made of dielectric insulating material, suchas silicon nitride or glass, and encircle the piezoelectric device 108mounted on the substrate 102 to prevent the conductive components of thepiezoelectric device 108 from electrical shorting.

FIG. 1C is a schematic top view of a piezoelectric device 130, which maybe a conventional type known in the art and can be used in place of thepiezoelectric device 108. FIG. 1D is a schematic cross-sectional view ofthe piezoelectric device 130 taken along the direction B-B of FIG. 1D.As shown FIGS. 1C-D, the piezoelectric device 130 includes: a bottomconductive flake 134; a piezoelectric layer 136; a top conductive flake132 connected to a wire 138 b; a connection flake 142 connected to awire 138 a; and a conducting segment 144 for connecting the connectionflake 142 to the bottom flake 134. The top conductive flake 132 may beelectrically separated from the connection flake 142 by a groove 140.

FIG. 1E is a schematic top cut-away view of a patch sensor 150 inaccordance with another embodiment of the present teachings. FIG. 1F isa schematic side cross-sectional view of the patch sensor 150 shown inFIG. 1E. As shown in FIGS. 1E-F, the patch sensor 150 may include: abottom substrate 151; a top substrate 152; a hoop layer 154; apiezoelectric device 156; top and bottom buffer layers 160 a-b; twoelectrical wires 158 a-b connected to the piezoelectric device 108. Thepiezoelectric device 156 includes: a piezoelectric layer 164; a bottomconductive flake 166 connected to the electrical wire 158 b; and a topconductive flake 162 connected to the electrical wire 158 a. Thefunctions and materials for the components of the patch sensor 150 maybe similar to those for their counterparts of the patch sensor 100. Eachof the buffer layers 160 a-b may include more than one sublayer and eachsublayer may be composed of polymer or metal. The top substrate 152 maybe made of the same material as that of the substrate 102.

The patch sensor 150 may be affixed to a host structure to monitor thestructural health conditions. Also, the patch sensor 150 may beincorporated within a laminate. FIG. 1G is a schematic cross-sectionalview of a composite laminate 170 having a patch sensor 150 therewithin.As illustrated in FIG. 1G, the host structure includes: a plurality ofplies 172; and at least one patch sensor 150 cured with the plurality ofplies 172. In one embodiment, the plies 172 may be impregnated withadhesive material, such as epoxy resin, prior to the curing process.During the curing process, the adhesive material from the plies 172 mayfill cavities 174. To obviate such accumulation of the adhesivematerial, the hoop layer 154 may have a configuration to fill the cavity174.

FIG. 1H is a schematic side cross-sectional view of an alternativeembodiment 180 of the patch sensor 150 of FIG. 1E. As illustrated, thepatch sensor 180 may include: a bottom substrate 182; a top substrate184; a hoop layer 198; a piezoelectric device 190; top and bottom bufferlayers 192 and 194; and the piezoelectric device 196. For simplicity, apair of wires connected to the piezoelectric device 190 is not shown inFIG. 1H. The piezoelectric device 190 may include: a piezoelectric layer196; a bottom conductive flake 194; and a top conductive flake 192. Thefunctions and materials for the components of the patch sensor 180 maybe similar to those of their counterparts of the patch sensor 150.

The hoop layer 198 may have one or more sublayers 197 of differentdimensions so that the outer contour of the hoop layer 198 may match thegeometry of cavity 174. By filling the cavity 174 with sublayers 197,the adhesive material may not be accumulated during the curing processof the laminate 170.

FIG. 2A is a schematic top cut-away view of a hybrid patch sensor 200 inaccordance with one embodiment of the present teachings. FIG. 2B is aschematic cross-sectional view of the hybrid patch sensor 200 takenalong a direction C-C of FIG. 2A. As shown in FIGS. 2A-B, the hybridpatch sensor 200 may include: a substrate 202 configured to attach to ahost structure; a hoop layer 204; a piezoelectric device 208; an opticalfiber coil 210 having two ends 214 a-b; a buffer layer 216; twoelectrical wires 212 a-b connected to the piezoelectric device 208; amolding layer 228; and a cover layer 206. The piezoelectric device 208includes: a piezoelectric layer 222; a bottom conductive flake 220connected to the electrical wire 212 b; and a top conductive flake 218connected to the electrical wire 212 a. In an alternative embodiment,the piezoelectric device 208 may be the same as the device 130 of FIG.1C. The optical fiber coil 210 may include; a rolled optical fiber cable224; and a coating layer 226. Components of the hybrid patch sensor 200may be similar to their counterparts of the patch sensor 100.

The optical fiber coil 210 may be a Sagnac interferometer and operate toreceive Lamb wave signals. The elastic strain on the surface of a hoststructure incurred by Lamb wave may be superimposed on the pre-existingstrain of the optical fiber cable 224 incurred by bending andtensioning. As a consequence, the amount of frequency/phase change inlight traveling through the optical fiber cable 224 may be dependent onthe total length of the optical fiber cable 224. In one embodiment,considering its good immunity to electromagnetic interference andvibrational noise, the optical fiber coil 210 may be used as the majorsensor while the piezoelectric device 208 can be used as an auxiliarysensor.

The optical fiber coil 210 exploits the principle of Doppler's effect onthe frequency of light traveling through the rolled optical fiber cable224. For each loop of the optical fiber coil 210, the inner side of theoptical fiber loop may be under compression while the outer side may beunder tension. These compression and tension may generate strain on theoptical fiber cable 224. The vibrational displacement or strain of thehost structure incurred by Lamb waves may be superimposed on the strainof the optical fiber cable 224. According to a birefringence equation,the reflection angle on the cladding surface of the optical fiber cable224 may be a function of the strain incurred by the compression and/ortension. Thus, the inner and outer side of each optical fiber loop maymake reflection angles different from that of a straight optical fiber,and consequently, the frequency of light may shift from a centered inputfrequency according to the relative flexural displacement of Lamb waveas light transmits through the optical fiber coil 210.

In one embodiment, the optical fiber coil 210 may include 10 to 30 turnsof the optical fiber cable 224 and have a smallest loop diameter 236,d_(i), of at least 10 mm. There may be a gap 234, d_(g), between theinnermost loop of the optical fiber coil 210 and the outer periphery ofthe piezoelectric device 208. The gap 234 may depend on the smallestloop diameter 236 and the diameter 232, d_(p), of the piezoelectricdevice 208, and be preferably larger than the diameter 232 by about twoor three times of the diameter 230, d_(f), of the optical fiber cable224.

The coating layer 226 may be comprised of a metallic or polymermaterial, preferably an epoxy, to increase the sensitivity of theoptical fiber coil 210 to the flexural displacement or strain of Lambwaves guided by its host structure. Furthermore, a controlled tensionalforce can be applied to the optical fiber cable 224 during the rollingprocess of the optical fiber cable 224 to give additional tensionalstress. The coating layer 226 may sustain the internal stress of therolled optical fiber cable 224 and allow a uniform in-plane displacementrelative to the flexural displacement of Lamb wave for each opticalloop.

The coating layer 226 may also be comprised of other material, such aspolyimide, aluminum, copper, gold or silver. The thickness of thecoating layer 226 may range from about 30% to two times of the diameter230. The coating layer 226 comprised of polymer material may be appliedin two ways. In one embodiment, a rolled optic fiber cable 224 may belaid on the substrate 202 and the polymer coating material may besprayed by a dispenser, such as Biodot spay-coater. In anotherembodiment, a rolled optic fiber cable 224 may be dipped into a moltenbath of the coating material.

Coating layer 226 comprised of metal may be applied by a conventionalmetallic coating technique, such as magnetron reactive orplasma-assisted sputtering as well as electrolysis. Specially, the zincoxide can be used as the coating material of the coating layer 226 toprovide the piezoelectric characteristic for the coating layer 226. Whenzinc oxide is applied to top and bottom surfaces of the rolled opticalfiber cable 224, the optical fiber coil 210 may contract or expandconcentrically in radial direction responding to electrical signals.Furthermore, the coating material of silicon oxide or tantalum oxide canalso be used to control the refractive index of the rolled fiber opticalcable 224. Silicon oxide or tantalum oxide may be applied using theindirect/direct ion beam-assisted deposition technique or electron beamvapor deposition technique. It is noted that other methods may be usedfor applying the coating layer 226 to the optical fiber cable 224without deviating from the present teachings.

The piezoelectric device 208 and the optical fiber coil 210 may beaffixed to the substrate 202 using physically setting adhesives insteadof common polymers, where the physically setting adhesives may include,but not limited to, butylacrylate-ethylacrylate copolymer,styrene-butadiene-isoprene terpolymer and polyurethane alkyd resin. Theadhesive properties of these materials may remain constant during andafter the coating process due to the lack of cross-linking in thepolymeric structure. Furthermore, those adhesives may be optimized forwetting a wide range of substrate 202 without compromising theirsensitivity to different analytes, compared to conventional polymers.

FIG. 2C is a schematic top cut-away view of a hybrid patch sensor 240 inaccordance with another embodiment of the present teachings. FIG. 2D isa schematic side cross-sectional view of the hybrid patch sensor 240shown in FIG. 2C. As shown in FIGS. 2C-D, the hybrid patch sensor 240may include: a bottom substrate 254; a top substrate 242; a hoop layer244; a piezoelectric device 248; an optical fiber coil 246 having twoends 250 a-b; top and bottom buffer layers 260 a-b; and two electricalwires 252 a-b connected to the piezoelectric device 248. Thepiezoelectric device 248 includes: a piezoelectric layer 264; a bottomconductive flake 262 connected to the electrical wire 252 b; and a topconductive flake 266 connected to the electrical wire 252 a. The opticalfiber coil 246 may include; a rolled optical fiber cable 258; and acoating layer 256. Components of the hybrid patch sensor 240 may besimilar to their counterparts of the hybrid patch sensor 200.

As in the case of the patch sensor 150, the hybrid patch sensor 240 maybe affixed to a host structure and/or incorporated within a compositelaminate. In one embodiment, the hoop layer 244 may be similar to thehoop layer 198 to fill the cavity formed by the patch sensor 240 and thecomposite laminate.

FIG. 3A a schematic top cut-away view of an optical fiber patch sensor300 in accordance with one embodiment of the present teachings. FIG. 3Ba schematic side cross-sectional view of the optical fiber patch sensor300 taken along the direction D-D of FIG. 3A. As shown in FIGS. 3A-B,the optical fiber patch sensor 300 may include: a substrate 302; a hooplayer 304; an optical fiber coil 308 having two ends 310 a-b; a moldinglayer 316; and a cover layer 306. The optical fiber coil 308 mayinclude; a rolled optical fiber cable 312; and a coating layer 314. Thematerial and function of each element of the optical fiber patch sensor300 may be similar to those of its counterpart of the hybrid patchsensor 200 in FIG. 2A. The diameter 313 of the innermost loop may bedetermined by the material property of the optic fiber cable 312.

FIG. 3C a schematic top cut-away view of the optical fiber coil 308contained in the optical fiber patch sensor of FIG. 3A, illustrating amethod for rolling the optical fiber cable 312. As shown in FIG. 3C, theoutermost loop of the optical fiber coil 308 may start with one end 310a while the innermost loop may end with the other end 310 b. FIG. 3D aschematic top cut-away view of an alternative embodiment 318 of theoptical fiber coil 308 shown in FIG. 3C. As shown in FIG. 3D, theoptical fiber cable 322 may be folded and rolled in such a manner thatthe outermost loops may start with both ends 320 a-b. The rolled opticalfiber cable 322 may be covered by a coating layer 319.

It is noted that the optical fiber coils 308 and 318 show in FIGS. 3C-Dmay be attached directly to a host structure and used as optical fibercoil sensors. For this reason, hereinafter, the terms “optical fibercoil” and “optical fiber coil sensor” will be used interchangeably.FIGS. 3E-F are alternative embodiments of the optical fiber coil 308. Asillustrated in FIG. 3E, the optical fiber coil 330 may include: anoptical fiber cable 334 having two ends 338 a-b and being rolled in thesame manner as the cable 312; and a coating layer 332. The coil 330 mayhave a hole 336 to accommodate a fastener as will be explained later.Likewise, the optical fiber coil 340 in FIG. 3F may include: an opticalfiber cable 344 having two ends 348 a-b and being rolled in the samemanner as the cable 322; and a coating layer 342. The coil 340 may havea hole 346 to accommodate a fastener. FIG. 3G is a schematic sidecross-sectional view of the optical fiber coil 330 taken along thedirection DD of FIG. 3E.

It should be noted that the sensors described in FIG. 3A-G may beincorporated within a laminate in a similar manner as described in FIG.1G.

FIG. 4A a schematic top cut-away view of a diagnostic patch washer 400in accordance with one embodiment of the present teachings. FIG. 4B aschematic side cross-sectional view of the diagnostic patch washer 400taken along the direction E-E of FIG. 4A. As shown in FIGS. 4A-B, thediagnostic patch washer 400 may include: an optical fiber coil 404having two ends 410 a-b; a piezoelectric device 406; a support element402 for containing the optical fiber coil 404 and the piezoelectricdevice 406, the coil 404 and the device 406 being affixed to the supportelement 402 by adhesive material; a pair of electrical wires 408 a-bconnected to the piezoelectric device 406; and a covering disk 414configured to cover the optical fiber coil 404 and the piezoelectricdevice 406.

The material and function of the optical fiber coil 404 and thepiezoelectric device 406 may be similar to those of the optical fibercoil 210 and the piezoelectric device 208 of the hybrid patch sensor200. In one embodiment, the piezoelectric device 406 may be similar tothe device 130, except that the device 406 has a hole 403. The opticalfiber coil 404 and the piezoelectric device 406 may be affixed to thesupport element 402 using a conventional epoxy. The support element 402may have a notch 412, through which the ends 410 a-b of the opticalfiber coil 404 and the pair of electrical wires 408 a-b may pass.

In FIGS. 4A-B, the diagnostic patch washer 400 may operate as anactuator/sensor and have the optical fiber coil 404 and thepiezoelectric device 406. In an alternative embodiment, the diagnosticpatch washer 400 may operate as a sensor and have the optical fiber coil404 only. In another alternative embodiment, the diagnostic patch washer400 may operate as an actuator/sensor and have the piezoelectric device406 only.

As shown in FIGS. 4A-B, the diagnostic patch washer 400 may have ahollow space 403 to accommodate other fastening device, such as a boltor rivet. FIG. 4C is a schematic diagram of an exemplary bolt-jointedstructure 420 using the diagnostic patch washer 400 in accordance withone embodiment of the present teachings. In the bolt-jointed structure420, a conventional bolt 424, nut 426 and washer 428 may be used to holda pair of structures 422 a-b, such as plates. It is well known thatstructural stress may be concentrated near a bolt-jointed area 429 andprone to structural damages. The diagnostic patch washer 400 may beincorporated in the bolt-joint structure 420 and used to detect suchdamages.

FIG. 4D is a schematic cross-sectional diagram of an exemplarybolt-jointed structure 430 using the diagnostic patch washer 400 inaccordance with another embodiment of the present teachings. In thebolt-joint structure 430, a conventional bolt 432, nut 434 and a pair ofwashers 436 and 438 may be used to hold a honeycomb/laminated structure440. The honeycomb and laminate structure 440 may include a compositelaminate layer 422 and a honeycomb portion 448. To detect the structuraldamages near the bolt-joint area, a pair of diagnostic patch washers 400a-b may be inserted within the honeycomb portion 448, as illustrated inFIG. 4D. A sleeve 446 may be required to support the top and bottompatch washers 400 a-b against the composite laminate layer 442. Also, athermal-protection circular disk 444 may be inserted between thecomposite laminate layer 422 and the diagnostic patch washer 400 b toprotect the washer 400 b from destructive heat transfer.

As shown in FIG. 4B, the outer perimeter 415 of the covering disk 414may have a slant angle to form a locking mechanism, which can keepoptical fiber coil 404 and the piezoelectric device 406 from excessivecontact load by the torque applied to the bolt 424 and nut 426.

FIG. 5A is a schematic diagram of an interrogation system 500 includinga sensor/actuator device in accordance with one embodiment of thepresent teachings. As shown in FIG. 5A, the system 500 may include: asensor/actuator device 502 for generating and/or receiving Lamb wavesignals; a two-conductor electrical wire 516; a conditioner 508 forprocessing signals received by the device 502; analog-to-digital (A/D)converter 504 for converting analog signals to digital signals; acomputer 514 for managing entire elements of the system 500; anamplifier 506; a waveform generator 510 for converting digital signalsinto the analog Lamb wave signals; and a relay switch array module 512configured to switch connections between the device 502 and the computer514. In general, more than one device 502 may be connected to the relayswitch 512.

The device 502 may be one of the sensors described in FIGS. 1A-2D andFIGS. 4A-D that may include a piezoelectric device for generating Lambwaves 517 and receiving Lamb waves generated by other devices. Togenerate Lamb waves 517, a waveform generator 510 may receive thedigital signals of the excitation waveforms from computer 514 (morespecifically, an analog output card included in the computer 514)through the relay switch array module 512. In one embodiment, thewaveform generator 510 may be an analog output card.

The relay switch array module 512 may be a conventional plug-in relayboard. As a “cross-talks” linker between the actuators and sensors, therelay switches included in the relay switch array module 512 may becoordinated by the microprocessor of the computer 514 to select eachrelay switch in a specific sequencing order. In one embodiment, analogsignals generated by the waveform generator 510 may be sent to otheractuator(s) through a branching electric wire 515.

The device 502 may function as a sensor for receiving Lamb waves. Thereceived signals may be sent to the conditioner 508 that may adjust thesignal voltage and filter electrical noise to select meaningful signalswithin an appropriate frequency bandwidth. Then, the filtered signal maybe sent to the analog-to-digital converter 504, which may be a digitalinput card. The digital signals from the analog-to-digital converter 504may be transmitted through the relay switch array module 512 to thecomputer 514 for further analysis.

FIG. 5B is a schematic diagram of an interrogation system 520 includinga sensor in accordance with another embodiment of the present teachings.The system 520 may include: a sensor 522 having an optical fiber coil;optical fiber cable 525 for connections; a laser source 528 forproviding a carrier input signal; a pair of modulators 526 and 534; anacoustical optic modulator (AOM) 530; a pair of coupler 524 and 532; aphoto detector 536 for sensing the light signal transmitted through theoptical fiber cable 525; an A/D converter 538; a relay switch 540; and acomputer 542. The sensor 522 may be one of the sensors described inFIGS. 2A-4D that may include an optical fiber coil. In one embodiment,the coupler 524 may couple the optical fiber cable 525 to anotheroptical fiber 527 that may be connected to another sensor 523.

The sensor 522, more specifically the optic fiber coil included in thesensor 522, may operate as a laser Doppler velocitimeter (LDV). Thelaser source 528, preferably a diode laser, may emit an input carrierlight signal to the modulator 526. The modulator 526 may be a heterodynemodulator and split the carrier input signal into two signals; one forthe sensor 522 and the other for AOM 530. The sensor 522 may shift theinput carrier signal by a Doppler's frequency corresponding to Lamb wavesignals and transmit it to the modulator 534, where the modulator 534may be a heterodyne synchronizer. The modulator 534 may demodulate thetransmitted light to remove the carrier frequency of light. The photodetector 536, preferably a photo diode, may convert the demodulatedlight signal into an electrical signal. Then, the A/D converter 538 maydigitize the electrical signal and transmit to the computer 542 via therelay switch array module 540. In one embodiment, the coupler 532 maycouple an optical fiber cable 546 connected to another sensor 544.

FIG. 6A is a schematic diagram of a diagnostic network patch system(DNP) 600 applied to a host structure 610 in accordance with oneembodiment of the present teachings. As illustrated in FIG. 6A, thesystem 600 may include: patches 602; transmission links 612; at leastone bridge box 604 connected to the transmission links 612; a dataacquisition system 606; and a computer 608 for managing the DNP system600. The patches 602 may be a device 502 or a sensor 522, where the typeof transmission links 612 may be determined by the type of the patches602 and include electrical wires, optical fiber cables, or both.Typically, the host structure 610 may be made of composite or metallicmaterial.

Transmission links 612 may be terminated at the bridge box 604. Thebridge box 604 may connect the patches 602 to admit signals from anexternal waveform generator 510 and to send received signals to anexternal A/D converter 504. The bridge box 604 may be connected throughan electrical/optical cable and can contain an electronic conditioner508 for conditioning actuating signals, filtering received signals, andconverting fiber optic signals to electrical signals. Using the relayswitch array module 512, the data acquisition system 606 coupled to thebridge box 604 can relay the patches 602 and multiplex received signalsfrom the patches 602 into the channels in a predetermined sequenceorder.

It is well known that the generation and detection of Lamb waves isinfluenced by the locations of actuators and sensors on a hoststructure. Thus, the patches 602 should be properly paired in a networkconfiguration to maximize the usage of Lamb waves for damageidentification.

FIG. 6B is a schematic diagram of a diagnostic network patch system 620having a strip network configuration in accordance with one embodimentof the present teachings. As shown in FIG. 6B, the system 620 may beapplied to a host structure 621 and include: patches 622; a bridge box624 connected to a computer 626; and transmission links 632. The patches622 may be a device 502 or a sensor 522, where the type of the patches622 may determine the type of transmission links 632. The transmissionlinks 632 may be electrical wires, optical fiber cables, or both.

The computer 626 may coordinate the operation of patches 622 such thatthey may function as actuators and/or sensors. Arrows 630 represents thepropagation of Lamb waves generated by patches 622. In general, defects628 in the host structure 621 may affect the transmission pattern in theterms of wave scattering, diffraction, and transmission loss of Lambwaves. The defects 628 may include damages, crack and delamination ofcomposite structures, etc. The defects 628 may be monitored by detectingthe changes in transmission pattern of Lamb waves captured by thepatches 622.

The network configuration of DNP system is important in Lamb-wave basedstructural health monitoring systems. In the network configuration ofDNP system 620, the wave-ray communication paths should be uniformlyrandomized. Uniformity of the communication paths and distance betweenthe patches 622 can determine the smallest detectible size of defects628 in the host structure 621. An optimized network configuration withappropriate patch arrangement may enhance the accuracy of the damageidentification without increasing the number of the patches 622.

Another configuration for building up wave ‘cross-talk’ paths betweenpatches may be a pentagonal network as shown in FIG. 6C. FIG. 6C is aschematic diagram of a diagnostic network patch system 640 having apentagon network configuration in accordance with another embodiment ofthe present teachings. The system 640 may be applied to a host structure652 and may include: patches 642; a bridge box 644 connected to acomputer 646; and transmission links 654. The patches 642 may be adevice 502 or a sensor 522. As in the system 630, the patches 642 maydetect a defect 650 by sending or receiving Lamb waves indicated by thearrows 648.

FIG. 6D is a schematic perspective view of a diagnostic network patchsystem 660 incorporated into rivet/bolt-jointed composite laminates 666and 668 in accordance with another embodiment of the present teachings.As illustrated in FIG. 6D, the system 660 may include: patches 662; anddiagnostic patch washers 664, each washer being coupled with a pair ofbolt and nut. For simplicity, a bridge box and transmission links arenot shown in FIG. 6D. The patches 662 may be a device 502 or a sensor522. In the system 660, the patches 662 and diagnostic patch washers 664may detect the defects 672 by sending or receiving Lamb waves asindicated by arrows 670. Typically, the defects 672 may develop near theholes for the fasteners. The diagnostic patch washers 664 maycommunicate with other neighborhood diagnostic patches 662 that may bearranged in a strip network configuration, as shown in FIG. 6D. In oneembodiment, the optical fiber coil sensors 330 and 340 may be used inplace of the diagnostic patch washers 664.

FIG. 6E is a schematic perspective view of a diagnostic network patchsystem 680 applied to a composite laminate 682 that may be repaired witha bonding patch 686 in accordance with one embodiment of the presentteachings. As illustrated in FIG. 6E, the system 680 may include patches684 that may be a device 502 or a sensor 522. For simplicity, a bridgebox and transmission links are not shown in FIG. 6E. In the system 680,the patches 684 may detect the defects 688 located between the repairpatch 686 and the composite laminate 682 by sending or receiving Lambwaves as indicated by arrows 687.

FIG. 6F is a schematic diagram illustrating an embodiment of a wirelessdata communication system 690 that controls a remote diagnostic networkpatch system in accordance with one embodiment of the present teachings.As illustrated in FIG. 6F, the system 690 includes: a bridge box 698;and a ground communication system 694 that may be operated by a groundcontrol 692. The bridge box 698 may be coupled to a diagnostic networkpatch system implemented to a host structure, such as an airplane 696,that may require extensive structural health monitoring.

The bridge box 698 may operate in two ways. In one embodiment, thebridge box 698 may operate as a signal emitter. In this embodiment, thebridge box 698 may comprise micro miniature transducers and amicroprocessor of a RF telemetry system that may send the structuralhealth monitoring information to the ground communication system 694 viawireless signals 693. In another embodiment, the bridge box 698 mayoperate as a receiver of electromagnetic waves. In this embodiment, thebridge box 698 may comprise an assembly for receiving power from theground communication system 694 via wireless signals 693, where thereceived power may be used to operate a DNP system applied to thestructure 696. The assembly may include a micro-machined siliconsubstrate that has stimulating electrodes, complementary metal oxidesemiconductor (CMOS), bipolar power regulation circuitry, hybrid chipcapacitors, and receiving antenna coils.

The structure of the bridge box 698 may be similar to the outer layer ofthe host structure 696. In one embodiment, the bridge box 698 may have amultilayered honeycomb sandwich structure, where a plurality of microstrip antennas are embedded in the outer faceplate of the multilayeredhoneycomb sandwich structure and operate as conformal load-bearingantennas. The multilayered honeycomb sandwich structure may comprise ahoneycomb core and multilayer dielectric laminates made of organicand/or inorganic materials, such as e-glass/epoxy, Kevlar/epoxy,graphite/epoxy, aluminum or steel. As the integrated micro-machiningtechnology evolves rapidly, the size and production cost of the microstrip antennas may be reduced further, which may translate to savings ofoperational/production costs of the bridge box 698 without compromisingits performance.

The scope of the invention is not intended to limit to the use of thestandard Wireless Application Protocol (WAP) and the wireless markuplanguages for a wireless structural health monitoring system. With amobile Internet toolkit, the application system can build a secure siteto which structural condition monitoring or infrastructure managementcan be correctly accessed by a WAP-enable cell phone, a Pocket PC with aHTML browser, or other HTML-enabled devices.

As a microphone array may be used to find the direction of a movingsource, a clustered sensor array may be used to find damaged locationsby measuring the difference in time of signal arrivals. FIG. 7A is aschematic diagram of a diagnostic network patch system 700 havingclustered sensors in a strip network configuration in accordance withone embodiment of the present teachings. As illustrated in FIG. 7A, thesystem 700 may be applied to a host structure 702 and include clusteredsensors 704 and transmission links 706. Each clustered sensor 704includes two receivers 708 and 712 and one actuator/receiver device 710.Each of the receivers 708 and 712 may be one of the sensors described inFIGS. 1A-4D, while the actuator/receiver device 710 may be one of thesensors described in FIGS. 1A-2D and FIGS. 4A-D and have a piezoelectricdevice for generating Lamb waves. When the actuator/receiver 710 of aclustered sensor 704 sends Lamb waves, the neighboring clustered sensors704 may receive the Lamb waves using all three elements, i.e., theactuator/receiver device 710 and receivers 708 and 712. By using allthree elements as a receiver unit, each clustered sensor 704 can receivemore refined Lamb wave signals. Also, by measuring the difference intime of arrivals between the three elements, the direction of the defect714 may be located with enhanced accuracy.

FIG. 7B is a schematic diagram of a diagnostic network patch system 720having clustered sensors in a pentagonal network configuration inaccordance with another embodiment of the present teachings. Asillustrated in FIG. 7B, the system 720 may be applied to a hoststructure 722 to detect a defect 734 and include clustered sensors 724and transmission links 726. Each clustered sensor 724 may be similar tothe clustered sensor 704.

FIG. 8A shows a schematic diagram of a clustered sensor 800 havingoptical fiber coils in a serial connection in accordance with oneembodiment of the present teachings. The clustered sensor 800 may besimilar to the clustered sensor 704 in FIG. 7A and include two sensors804 and 808 and an actuator/sensor 806. In this configuration, an inputsignal may enter the sensor through one end 810 a and the output signalfrom the other end 810 b may be a sum of the input signal andcontribution of the three sensors 804, 806 and 808. In one embodiment,the signal from each sensor may be separated from others using awavelength-based de-multiplex techniques.

FIG. 8B a schematic diagram of a clustered sensor 820 having opticalfiber coils in a parallel connection in accordance with one embodimentof the present teachings. The clustered sensor 820 may be similar to theclustered sensor 704 in FIG. 7A and include two sensors 824 and 828 andan actuator/sensor 826. In this configuration, input signals may enterthe three sensors through three end 830 a, 832 a and 834 a,respectively, while output signals from the other ends 830 b, 832 b and834 b may be a sum of the input signal and contribution of the threesensors 824, 826 and 828, respectively.

It is noted that, in FIGS. 8A-B, the sensors 804, 808, 824 and 828 havebeen illustrated as optical fiber coil sensors 308. However, it shouldapparent to those of ordinary skill in the art that each of the sensors804, 808, 824 and 828 may be one of the sensors described in FIGS.1A-4D, while each of the middle sensors 806 and 826 may be one of thesensors described in 1A-2D and FIGS. 4A-D and have a piezoelectricdevice for generating Lamb waves. Also, the clustered sensors 800 and820 may be incorporated within a composite laminate in the same manneras described in FIG. 1G.

FIG. 9 shows a plot 900 of actuator and sensor signals in accordancewith one embodiment of the present teachings. To generate Lamb waves, anactuator signal 904 may be applied to an actuator, such as a patchsensor 100. The actuator signal 904 may be a toneburst signal that hasseveral wave peaks with the highest amplitude in the mid of waveform andhas a spectrum energy of narrow frequency bandwidth. The actuator signal904 may be designed by the use of Hanning function on various waveformsand have its central frequency within 0.01 MHz to 1.0 MHz. When theactuator receives the actuator signal 904, it may generate Lamb waveshaving a specific excitation frequency.

Signals 912 a-n may represent sensor signals received by sensors. As canbe noticed, each signal 912 may have wave packets 926, 928 and 930separated by signal extracting windows (or, equivalently envelops) 920,922 and 924, respectively. These wave packets 926, 928 and 930 may havedifferent frequencies due to the dispersion modes at the sensorlocation. It is noted that the signal partitioning windows 916 have beenapplied to identify Lamb-wave signal from each sensor signal. The wavepackets 926, 928 and 930 correspond to a fundamental symmetric mode S₀,a reflected mode S₀ _(—) _(ref) and a fundamental antisymmetric mode A₀,respectively. The reflected mode S₀ _(—) _(ref) may represent thereflection of Lamb waves from a host structure boundary. A basic shearmode, S₀′, and other higher modes can be observed. However, they are notshown in FIG. 9 for simplicity.

Portions 914 of sensor signals 912 may be electrical noise due to thetoneburst actuator signal 904. To separate the portions 914 from therest of sensor signals 912, masking windows 918, which may be a sigmoidfunction delayed in the time period of actuation, may be applied tosensor signals 912 as threshold functions. Then, moving wave-envelopewindows 920, 922 and 924 along the time history of each sensor signalmay be employed to extract the wave packets 926, 928 and 930 from thesensor signal of 912. The wave packets 926, 928 and 930 may be thesensor part of the sensor signal 912. The envelope windows 920, 922 and924 may be determined by applying a hill-climbing algorithm thatsearches for peaks and valleys of the sensor signals 912 andinterpolating the searched data point in time axis. The magnitude andposition of each data point in the wave signal may be stored if themagnitude of the closest neighborhood data points are less than that ofthe current data point until the comparison of wave magnitude in theforward and backward direction continues to all the data points of thewave signal. Once envelopes of wave signals are obtained, each envelopemay break into sub envelope windows 920, 922 and 924 with time spanscorresponding to those of Lamb-wave modes. The sub envelop windows 920,922 and 924 may be applied to extract wave packets 926, 928 and 930 bymoving along the entire time history of each measured sensor signal 912.

Upon completion of applying a DNP system to a host structure, astructural health monitoring software may start processing the DNPsystem, where the monitoring software may comprise interrogation,processing, classification and prognosis modules. FIG. 10 is a flowchart 1000 illustrating exemplary procedures of the interrogation modulein accordance with one embodiment of the present teachings. Theinterrogating module may find damages, identify impacts and monitor thecuring and repaired-boning-patch performance of the host structures. Instep 1002, the interrogation module may partition the diagnostic patchesof the DNP system into subgroup sets, and designate one actuator in eachof the subgroups. It is noted that each of the diagnostic patches mayfunction as an actuator at one point in time, and thereafter the samepatch may be switched to function as a sensor. FIG. 11A illustrates anexample of actuator network architecture 1100 that may include subgroupspartitioned by the interrogation module in accordance with oneembodiment of the present teachings. As each of the actuators 1102,1104, 1106 and 1108 may also function as a sensor, various combinationsof subgroups can be formed of those actuators. Arrows 1110 represent thepropagation of Lamb wave signals between actuators 1102, 1104, 1106 and1108. Table 1 shows the possible subgroups, where each group has oneactuator. For example, subgroup 1 has one actuator A1 1102 and twosensors A2 1104 and A4 1108.

TABLE 1 Subgroups made of four patches in FIG. 11A Subgroup numberactuator sensors 1 A1 A2, A4 2 A2 A1, A3, A4 3 A3 A2, A4 4 A4 A1, A2, A3

FIG. 11B illustrates another example of actuator/sensor networkarchitecture 1120 that may include subgroups partitioned by theinterrogation module in accordance with another embodiment of thepresent teachings. As illustrated in FIG. 11B, four subgroups 1122,1124, 1126 and 1128 may be generated using four actuator/sensors 1132a-1132 d and thirteen sensors 1130 a-1130 m. Table 2 shows the elementsof each subgroup formed of the patches in FIG. 11B.

TABLE 2 Subgroups made of seventeen patches in FIG. 11B Subgroup numberactuator sensors 5 A1 A2, S1, S2, S3, S4 6 A2 A1, A4, S3, S4, S5, S6, S77 A3 A2, S6, S7, S8, S9, S10 8 A4 A2, S11, S12, S13

It is noted that each of the thirteen sensors 1130 a-1130 m in FIG. 11Bmay also function as an actuator. However, only one patch in eachsubgroup will operate as an actuator at one point in time while theother patches are synchronized to operate as sensors. As in the case ofFIG. 11B, one sensor (such as s3) may belong to more than one subgroup(group 5 and 6). In FIGS. 11A-B, only four actuator/sensors and thirteensensors are shown for clarity illustration. However, it should beapparent to those of ordinary skill that the present invention may bepracticed with any number of patches.

The network architecture of a diagnostic patch system, such as shown inFIGS. 11A-B, can be configured to maximize overall network performancewith the minimum number of the actuators and sensors. The diagnosticnetwork can be represented by an undirected graph G=(N, E), in which thenodes N and edges E represent the patch sites and wave-communicationpaths, respectively. The graph G may be a picture of the relation of thediagnostic network communication, whereas the node points 1102, 1104,1006, 1108 in FIG. 11A represent the elements of actuator and sensorsets, and solid lines as edges 1110 in FIG. 11A represent the orderedpairs in the relation from the actuator set and the sensor set in theTable 1. A graph G is connected if there is at least one path betweenevery pair of nodes i and j. In an exemplary optimal design for networkpath uniformity, the following notation may be defined: n is the numberof nodes; X_(ij)ε{0,1} is a decision variable representing paths betweennodes i and j; and x(={x₁₂, x₁₃, . . . , x_(n−1,n)}) is a topologicalarchitecture of network design. R(x) is a constraint of network design,such as the number of the patches; c_(ij) is the cost variable of thenetwork design, such as the distance of Lamb wave propagation, thenumber of intersection points on each network path being crossed byother network paths or the sensitivity factor to excitation frequency.The optimal design of diagnostic network can be represented as follows:

${{Z(x)} = {{\sum\limits_{i = 1}^{n - 1}{\sum\limits_{j = {i + 1}}^{n}{c_{ij}x_{ij}\mspace{14mu}{s.t.\mspace{14mu}{R(x)}}}}} \geq {R_{\min}.}}},$where this optimal problem must be solved for the values of thevariables x(={x₁₂, x₁₃, . . . . , x_(n−1,n)}) that satisfy therestriction R_(min) and meanwhile minimize the objective function Z(x)representing network path uniformity.

In another example of optimal group design, each of the sensors in anetwork subgroup may be associated with one actuator of the group asillustrated in FIG. 11B. The network performance may depend on theposition and the number of the actuator and sensors in each subgroup.For this group layout of patches, an actuator/sensor matrix may beconsidered, where each element (i,k) of the matrix is 1 if the i^(th)sensor is associated by the k^(th) actuator and 0 otherwise. In such agroup design, a common integer programming formulation, which consistsof the following assignment of variable declarations and constraint, maybe applied. Each actuator may be assigned to only one subgroup, whereeach sensor may assigned to more than one subgroup: x_(ic) is 1 ifi^(th) actuator is assigned to subgroup c and 0 otherwise; y_(ic) is 1if j^(th) sensor is assigned to subgroup c and 0 otherwise. Twoconstraints may be expressed as:

${{\overset{k}{\sum\limits_{c = 1}}x_{ic}} = 1},{i = 1},\ldots\mspace{11mu},{{m\mspace{14mu}{and}\mspace{14mu}{\sum\limits_{c = 1}^{k}y_{jc}}} = 1},{i = 1},\ldots\mspace{11mu},n,$where k is the number of subgroups specified, and m, n are the number ofactuators and sensors, respectively.

Referring back to FIG. 10, a genetic algorithm implemented in theinterrogation module may design the network and signal paths in step1004. One or more artificial defects, such as small detachable patches,may be applied to the host structure to simulate damages. Then, eachactuator may send signal to sensors in one or more of the partitionedsubgroups. Based on the signals received by the sensors, the geneticalgorithm may determine the optimum network, signal paths and sequenceorder of the actuators so that the locations and types of the artificialdefects can be accurately detected. Depending on the geometrical shapesand materials of the host structure, the determination of subgroup setsmay include the step of adjusting the number of actuator/sensors in thecommunication network.

In step 1006, the actuator in i^(th) subgroup may be activated togenerate the Lamb wave signals according to the sequential order fromthe relay switch array module 512 (shown in FG. 5A). Then, the signalscarrying structural condition information (SCI) may be measured by thesensors of j^(th) subgroup in step 1008, where the j^(th) subgroup mayinclude the i^(th) subgroup. In step 1010, the interrogation module maycompute the deviation of the measured signals from baseline signals,wherein the baseline signals may be prepared by performing the steps1004 and 1006 in the absence of the artificial defects. Next, theinterrogation module may store the deviation and measured signals into asuitable signal database depository (such as computer 514) as extensibleMarkup Language (XML) formatted documents in step 1012. In addition, theinterrogation module may save the coordinates of the actuators andsensors as well as the set-up information data including the actuationfrequency, the identification number of the actuators and sensors,voltage level, patch type and operational failure status. Subsequently,the interrogation module may stop the interrogation process in step1014.

The interrogation module may perform the steps 1006, 1008, 1010 and 1012at discrete set of excitation frequencies, where the actuators of theDNP system may be activated at each excitation frequency to generateLamb waves. Then, the process module may process the stored sensorsignals to determine structural condition index (SCI) for each networkpath at an excitation frequency. The SCI of a network path between apair of actuator and sensor refers to a quantity that may be affected bydamages of the host structure and, as a consequence, represent thedegree of structural condition changes probably located in the interiorregion of the host structure. The SCI may include, but not limited to,the time of arrivals for Lamb wave modes, the spectrum energy for Lambwave modes distributed on their time-frequency domain or peak amplitudeof sensor signal. FIG. 12 is a flow chart illustrating exemplaryprocedures 1200 for identifying and determining the time of arrivals forLamb wave modes in accordance with one embodiment of the presentteachings. In step 1202, the process module may load a set of sensorsignal data from a signal database depository, such as computer 514,where each sensor signal data may be measured at one excitationfrequency. Hereinafter, the excitation frequency refers to a frequencyat which the actuators of the DNP system are activated to generate Lambwaves. The stored set-up information data for the network patch systemmay be checked and the network path numbers may be identified to checkwhether the appropriate actuator and sensor are assigned to the path ofeach network link. Then, in step 1204, the process module may detrendeach of the loaded sensor signal data to remove a non-stationary signalcomponent. Next, in step 1206, the electrical noise 914 due to thetoneburst actuator signal 904 may be removed by applying a maskingwindow 918 to the detrended each signal data. Subsequently, in step1208, the short-time Fourier or wavelet transformation may be performedon the noise-removed signal data to obtain a time-frequency signalenergy distribution about the center frequency bandwidth of excitationalong the time axis.

In step 1210, the process module may perform accumulating entire set oftime-frequency signal energy distributions to generate a multi-bandwidthenergy distribution on the time-frequency plane. Then, in step 1212, theprocess module may extract ridges (curves) from the multi-bandwidthenergy distribution on the time-frequency plane. The ridges extractedfrom this energy distribution can show the trajectory curve of each wavemode and provide the local maxima along the frequency axis. In the ridgeextraction, searching the local maxima may be done on a fixed value intime axis where the maximum in the row of the distribution data may becompared to new two rows given by shifting the row one-step in bothdirections and this maximum may be stored if it is greater than apredefined threshold. In step 1214, based on the ridge curves, theprocess module may identify the trajectory of the S₀, S₀ _(—) _(ref) andA₀ mode waves (926, 928 and 930 of FIG. 9) on the time-frequency plane.Then, the process module may stop identifying process in step 1216.

As will be explained later, the trajectory of the S₀, S₀ _(—) _(ref) andA₀ mode waves determined in step 1214 may be utilized for designing themoving envelope windows of various time spans with respect to the modewaves. The ridge extraction method may provide an accurate determinationof the arrival time of each mode wave so that the phase velocities andthe arrival-time differences between these modes can be exactly computedinstead of calling for a dispersion curve formula of the structure. Itis noted that the scope of the invention is not limited to the use ofwavelet transformation in the time-frequency interpretation method.

FIGS. 13A-B show a flow chart 1300 illustrating exemplary procedures forcomputing SCI values (or, equivalently, damage index values) inaccordance with one embodiment of the present teachings. To compute SCIvalues, the process module may use the sensor signal dataset measured ata set of excitation frequencies. In step 1302, the process module mayload a plurality of sensor signal datasets, where each sensor signaldataset is measured at one excitation frequency, where each sensorsignal of a dataset, such as the signal 912, may correspond to a networkpath of the DNP system. Then, in step 1304, one of the plurality ofsensor signal datasets may be selected. Subsequently, a sensor signalmay be selected from the selected sensor signal data set in step 1306.In step 1308, the selected sensor signal may be detrended by applying amoving-average filter and partitioned into the actuation part 914 andreceiving part 916 by applying a masking window 918 (shown in FIG. 9).In step 1310, the sensor signal may be decomposed into the severalsub-bandwidth wave packets 926, 928 and 930 by a wavelet decompositionfilter that preferably uses the Daubechies wavelet filter coefficients.For sub-bandwidth wave packet decomposition, a dyadic filter is designedfor the Daubechies wavelet filter coefficients to provide high and lowdecomposition, and high and low reconstruction filter. The decompositionfilter decomposes the detrended signal into the wavelet coefficients formultiresolution levels. Next, in step 1312, the process module maysynthesize new sub-bandwidth wave packets within the frequency range ofinterest, where the Lamb wave signal may contain the waves of S₀, S₀_(—) _(ref), and A₀ modes in the frequency bandwidth. The frequencyrange in a synthesized signal may be determined using a ridge extractionmethod to cover the range of the sub-bandwidth variation of each wavesignal such that the multi resolution levels selected in thereconstruction filter may correspond to the bandwidth of the synthesizedsignal containing the wave signals of S₀, S₀ _(—) _(ref), and A₀. Thesynthesized signal is then generated using the reconstruction filer andthe wavelet coefficients in signal decomposition. Then, in step 1314,the process module may apply signal extracting windows (or equivalently,moving envelop windows) 920, 922 and 924 to the synthesized Lamb wavesignal to extract the S₀, S₀ _(—) _(ref) and A₀ mode waves 926, 928 and930 as independent waveforms. Each of S₀, S₀ _(—) _(ref) and A₀ modewaves 926, 928 and 930 may be fitted within an envelop of each wavemode. In step 1316, the process module may determine the maximum, centerposition and span width of each of the envelope windows 920, 922 and 924in the time axis. Then, it may compute in step 1318 SCI for the selectedsensor signal. In one embodiment, the SCI may be based on the change inthe spectrum energy of each wave of the S₀, S₀ _(—) _(ref) and A₀ modes.In this embodiment, the process module may determine the spectrum energyof each wave of the S₀, S _(0—) _(ref) and A₀ modes. Next, the processmodule may calculate the summation of these spectrum energies of the S₀,S₀ _(—) _(ref) and A₀ modes and determine the difference in the summedenergies between the baseline and damaged conditions of the hoststructure. Consequently, the spectrum energy difference may be utilizedas a SCI value of the selected sensor signal. In an alternativeembodiment, the process module may choose the changes in the maximum andcenter positions of the envelope windows as the SCI values.

Moreover, if the diagnostic measurement system use traditional vibrationsensors such as accelerometers, displacement transducers or straingauges, the process module can compute structural dynamic parameters,such as natural frequencies, damping ratios or mode shapes, fromvibrational signal dataset obtained at a plurality of vibration sensorlocations. The process module may exploit the change in structuraldynamic parameters as the SCI values when traditional vibration sensorsignals are used instead of Lamb wave signals, as another alternativeembodiment.

After the process module computes the SCI data for all the networkpaths, it may remove abnormal sensor signals possibly included in twodatasets of the sensor signals corresponding to the baseline and damagedconditions of the host structure. For this purpose, the process modulemay evaluate whether each sensor signal may have reasonable distributionof signal amplitudes in terms of probability. In step 1320, the processsteps may determine the discrete probability density function (DPDF) onthe signal amplitudes, and estimate the 2nd, 3rd and 4th moments of

${1/N}{\sum\limits_{i = 1}^{N}\;{x_{i}^{2,3,4}{p\left( x_{i} \right)}}}$for the amplitude distribution p(x_(i)). From these estimates of theamplitude distribution, the covariance δ, skewness factor η, andflatness factor κ of the DPDF may be used to determine, in step 1322, anormality constant α on each sensor signal. The normality factor may bedefined in terms of the product of these factors with power weightings:α=δ^(3/2)η⁻²κ^(3/4). In step 1324, the process module may check if allof the sensor signals contained in the selected sensor signal datasethave been considered. Upon negative answer to the decision step 1324,the process may proceed to the step 1306. Otherwise, the process mayproceed to step 1326 in FIG. 13B.

In step 1326, the process module may compute the second PDF of a SCIdataset comprising SCI values for the sensor signals contained in theselected sensor signal dataset. Then, based on the second PDF, it mayfind the outliers of SCI values outside the 3-sigma of the SCIdistribution in step 1328. By checking the normality constants of theSCI outliers, the process module may delete the SCI values of theoutliers from the SCI dataset for more reliable structural healthmonitoring.

As the change in ambient temperature during the measurements of sensorsignals can influence the sensor signals of Lamb waves, the SCI valuesobtained from the Lamb wave sensor signals should be modified tocompensate for the difference in ambient temperatures between thebaseline and damaged structure conditions. The process module may checkwhether the measurement temperature of the baseline is different fromthat of the damaged structure condition. The process module may preparea temperature reference table of Lamb waves. To establish the referencetable, it may compute the time-span widths and maxima of the S₀-modeenvelopes for all of the network paths of the baseline structure anddetermine the average of the time-span width data for the 95% networkpaths in the envelope maximum distribution. With the help of thereference table, the process module can calculate atemperature-adjustment parameter as the average ratio of thetime-span-width in the baseline structure signals to the reference tablevalue corresponding to the temperature of the damaged structure. In step1330, the process module may compensate the effect of ambienttemperature change on the sensor signals by scaling the SCI data of thedamaged structure with the temperature-adjustment parameter. Next, theprocess module may store the SCI dataset as extensible MarkupLanguage(XML) formatted documents in step 1332. Subsequently, in adetermination step 1334, the process module may check if the SCI datasetfor each of the excitation frequencies have been generated. Uponnegative answer to the decision step 1334, the process may proceed tothe step 1304. Otherwise, the process module may stop the process instep 1336.

FIGS. 14A shows a flow chart 1400 illustrating exemplary procedures forgenerating a tomographic image to identify the regions having changes instructural conditions or damages in accordance with one embodiment ofthe present teachings. In step 1402, the process module may load thecoordinate data for diagnostic patches and SCI values for the networkpaths defined by the diagnostic patches. For any i^(th) network pathline, the bisection point of a network path may be calculated in step1404 from the actuator and sensor coordinates of {x_(i) ^(act), y_(i)^(act)} and {x_(i) ^(sen), y_(i) ^(sen)} as the half of the minimumdistance of the path line, tangential to the surface of the structuralgeometry. Then, the SCI value of the i^(th) network path may bedesignated to the bisection point of the i^(th) network path. Next, theprocess module may calculate intersection points of the network paths instep 1406. The process module may calculate the slope of m_(i)=(y_(i)^(sen)−y_(i) ^(act))/(x_(i) ^(sen)−x_(i) ^(act)), its inverse m_(i)=1/m_(i), and the constants of C_(i)=y_(i) ^(act)−m_(i)x_(i) ^(act)and C _(i)=x_(i) ^(act)− m _(i)y_(i) ^(act) for the j^(th) path line.Then, the process module may determine the coordinate{(C_(k)−C_(i))/(m_(i)−m_(k)), (m_(i)C_(k)−m_(k)C_(i))/(m_(i)−m_(k))} onthe i^(th) path line for all of the other k^(th) path lines intersectingthe i^(th) path line, with the condition on the slope m_(i) to meet(C_(k)+m_(x)x_(k) ^(sen)+y_(k) ^(sen))/( C _(k)+ m _(k)y_(k)^(sen)−x_(k) ^(sen))≦m_(i)≦( C _(k)+m_(k)x_(k) ^(act)−y_(k)^(act))/(C_(k)+ m _(k)y_(k) ^(act)−x_(k) ^(act)). In step 1408, theprocess module may calculate the product of SCI values of the i^(th) andk^(th) network paths to assign a new SCI on each of the intersectionpoints. In the case of no intersection, the designated SCI may be thehalf of the SCI value of the i^(th) path line and the intersection pointmay be the same as the bisection point. Thus, the SCI values consideredas the z-axis data on the coordinate plane of the actuators and sensorsin the network path lines may be assigned to all of the bisection andintersection points. In one embodiment, the SCI data of all thebisection and intersection points may be stored as extensible MarkupLanguage(XML) formatted documents into a SCI database depository.

For any i^(th) path line, the process module may set a z-axis Gaussianor generalized bell function in the plane normal to the path linedirection such that the maximum at the center of the Gaussian functionmay be the SCI value of the path. In step 1410, this z-axis function maybe used to create a 3-dimensional block on the network path coordinateplane, in the manner that the cross section of the Gaussian function mayrun in parallel to the path line from the beginning and the end of thepath line. Actually, this 3-dimsional function of the i^(th) path linemay intersect by being overlapped with other 3-dimensional functions ofany other k^(th) path lines. The SCI values at the intersection area maybe determined by the product of the intersecting Gaussian SCI functionson the network path coordinate plane. The width of this 3-dimensionalfunction in the cross-section plane may be the shortest distance in allthe path lines, which is multiplied by the SCI value ratio of the i^(th)path to the shortest distance path line. The process module may continueto compute the SCI values on the network plane until all the networkpaths are considered. In step 1412, the process module may interpolatethe SCI dataset for each of the bisection, the intersection and the3-dim Gaussian-function overlapping points over the mesh-grid points,made by dividing the entire region of the structure into small meshelements. In this interpolation, the process module may employ theDelaunay triangulation of the convex-hull set for the grid data of SCIvalues.

By applying a genetic algorithm, the process module may further refinethe SCI distribution on the network path plane to precisely locate thedamaged regions in the host structure. In step 1414, the process modulemay setup an initial population of chromosome and assign each chromosometo a corresponding one of the mesh-grid points. Then, in step 1416, theprocess module may rank the chromosomes by evaluating them with thecorrelation of the SCI distribution data of the neighboring grid points.In step 1418, the process module may select parents from the populationusing a random-selection procedure biased so that the parents withhighest evaluations are most likely to reproduce. The process module mayalso reproduce children from some combination of the parents so thatpossible random mutation of children takes place. Then, in step 1420,the parent chromosomes may be replaced by the children chromosomes.Steps 1416-1420 may be repeated over a number of generations until acomplete new population of children is established in step 1422, wherethe children may be evaluated and the entire population of parents isreplaced to become parents themselves. Then, in step 1424, the processmodule may get the refined SCI distribution on the gird points with thecomposition of the final population of chromosomes.

The SCI distribution on the mesh-grid points corresponding to the finalchromosomes may represent the degree of changes in the structuralcondition of the host structure. The regions of area where thestructural condition changes or damages may occur in the host structurecan be exactly identified from this refined SCI distribution. In step1426, for the structural condition or damage identification of the hoststructure, the process module can provide a genetic-based tomographyimage using the interpolated SCI distribution. Also, by repeating thesteps 1402-1426 at a set of excitation frequencies, a set of tomographicimages may be obtained.

FIG. 14B is a flow chart 1430 illustrating exemplary procedures forgenerating a tomographic image to identify regions having changes instructural conditions or damages in accordance with another embodimentof the present teachings. In step 1432, the process module may load atime of arrival dataset of a Lamb wave mode, such as SO mode. Asmentioned, the time of arrival for a Lamb wave mode can be used as aSCI. Using the extracted ridge curves in step 1212, the process modulemay exactly determine for all the network paths the time-of-arrivaldifferences between the Lamb wave modes. Next, in step 1434, aconventional algebraic reconstruction technique may be applied to theloaded time of arrival dataset for the global inspection of damage onthe host structure. Then, based on the reconstructed time-of-arrivaldata, a tomography of the entire region of the host structure may begenerated in step 1436. In one embodiment, the steps 1432-1436 may berepeated to generate a set of tomographic images of the entire region,where each tomographic image may be based on a time-of-arrival datasetmeasured at a different excitation frequency. By stacking the set oftomographic images, a hyperspectral tomography cube of the entire regionmay be obtained.

The process module can also employ a simultaneous iterativereconstruction technique to investigate the defect characteristics of asuspicious region of the host structure. In step 1438, the network pathsmay be rearranged to focus on a suspicious region. Then, in step 1440,the process module may apply the simultaneous iterative reconstructiontechnique to the loaded time-of-arrival dataset to investigate thedefect characteristics of the suspicious region. Next, based on thereconstructed dataset, a tomographic image of the suspicious region maybe generated in step 1442. In one embodiment, the steps 1432-1442 may berepeated to generate a set of tomographic images for the suspiciousregion, where each tomographic image may be based on a time-of-arrivaldataset measured at a different excitation frequency. By stacking theset of tomographic images, a hyperspectral tomography cube of thesuspicious region may be obtained.

In another embodiment, the genetic-based distribution on thetime-of-arrival dataset of the network paths, incorporated with theridge extraction method for the short-time-Fourier-transformation (STFT)of sensor signals, may be also used to determine the SCI distributionand generate a tomographic image. In this embodiment, the tomographicimage may be different from those in the steps 1436 or 1442. The methodof the ridge extraction and genetic-based distribution for Lamb-wavetime-of-arrival dataset can employ the scattering-operator-eigenfunctionbased tomography-imaging techniques known in the art.

When the process module displays a color tomographic image, the range ofcolors may be adjusted to enhance the visibility of the ‘hot-spot’ zoneshaving damage with respect to the background color. In addition, thetomographic image can have colored marks and dotted lines to show thelocations of actuators and sensors and the network path lines over a 2or 3-dimensional image of the structural geometry. The process modulemay store the tomographic images as well as the range of colors into atomography database depository. FIG. 14C shows an example of tomographyimage 1450 obtained in step 1426, where the image is expressed in a grayscale. As can be noticed, the regions 1452 may represent damages.

FIG. 14D shows a hyperspectral tomography cube 1460 in accordance withone embodiment of the present teachings. As illustrates in FIG. 14D, thehyper spectral tomography cube 1460 comprises layers of two-dimensionaltomographic images 1462, 1464 and 1466, where each image may begenerated at an excitation frequency and the z-axis may represent theexcitation frequency. For simplicity, only three layers 1462, 1464 and1466 are shown in FIG. 14D. However, it should be apparent to those ofordinary skill that the hyperspectral tomography cube 1460 may compriseimage layers generated at continuous excitation frequency range.

FIG. 14E shows a 3-dimensional damage evolution manifold 1470illustrating the variation of structural condition in accordance withone embodiment of the invention. Like the hyperspectral tomography cube1460, the manifold 1470 may comprise two-dimensional tomographic imagesstacked in z-direction, wherein each image is generated after a numberof vibrational repetition cycles corresponding to the z-value has beenapplied to the host structure. Also, in each tomographic image, only aportion that shows structural changes has been displayed. Thus, each ofslices on the 3-dim damage-evolution manifold 1470 may represent theevolution state of structural condition or damage in a structure.

As mentioned in the step 1410 of FIG. 14A, the process module maydetermine SCI values near the intersection points of network paths. Aclassification module that includes a neuro-fuzzy inference system mayalso determine the SCI values at the intersection points. FIG. 15 is aschematic diagram 1500 illustrating procedures of a neuro-fuzzyinference system for providing structured system condition index (SCI)distribution at the intersection points of network paths in accordancewith one embodiment of the invention. As explained in step 1408, each ofthe intersection points in the network paths has two crossing path lineswith their SCI values and distances. To obtain structured SCI values atthe intersection points, the distance of two crossing path lines may beexploited by a fuzzy if-then rule system collaborated with a neuralnetwork. Then, this expert system may generate the output of SCI valuesof the intersecting paths.

For any of the n intersection points P1-Pn 1502, each of two crossingpath line distances 1504 can be input into three fuzzy membershipfunctions 1506, (A₁/B₁, A₂/B₂, A₃/B₃), in the terms of “short”,“medium”, “long” distance. For the membership function, generalized bellfunctions of μ_(A) ₁ _(/B) ₁ =1/[1+|(x−c_(i))/α_(i)|^(2b)], i=1, 2, 3,may be used with the adjustment parameters of (a, c) to cover each inputregion of the path line distance normalized to a structure dimension. Inlayer 1508, every node may be a fixed node labeled Π and generate anoutput v_(i) ^(k) that may be the product of the incoming signals ofA_(i), B_(i): v_(i) ^(k)=μ_(A) _(i) (x^(k))μ_(B) _(i) (x^(k)), k=1, . .. , n. Each node output may represent the firing strength of a rule. Anyi^(th) node of layer 1510, labeled N, may calculate the ratio w_(i) ^(k)of the i^(th) rule's firing strength to the sum of all rules' firingstrengths: w_(i) ^(k)=v_(i) ^(k)/(v_(i) ^(k)+v₂ ^(k)+v₃ ^(k)), i=1, 2, 3so that the output w_(i) ^(k) of the layer 1510 may be a normalizedfiring strength. Moreover, SCI values of step 1408 at intersecting pathsin layer 1512 may be inputted into a multilayer perception or neuralnetwork. In layer 1514, each node may be adapted with a node functionof, c_(i) ^(k)=f_(i) ^(k)(s₁ ^(k),s₂ ^(k)), i=1, 2,3 where c_(i) ^(k) isthe consequent parts in a network-layered representation which can becompared with a simple backpropagation multilayer perception with theinput layer 1512 of SCI values s_(i) ^(k). Here, f_(i) ^(k)(s₁ ^(k),s₂^(k)) require two SCI values of the intersecting path lines as input. Ifall three neurons 1514 and one neuron 1516 have identity functions inFIG. 15A, the presented neuro-fuzzy is equivalent to Sugeno (TSK) fuzzyinference system, which accomplishes linear fuzzy if-then rules.Adjusting the relevant connection strengths or weighting factors on theneural network link according to the error distance may initiate theadaptation in the neural network. In one embodiment, a sigmoidalfunction may be used as the neuron function in the consequent layer1514. In another embodiment, the neural network layer can use a backpropagation multilayer perception and radial basis function networks. Inlayer 1516 as an output of the consequent layer 1514, the node maycompute the summation of all incoming signals like y^(k)=Σ_(i)w_(i)^(k)c_(i) ^(k)/Σ_(i)w_(i) ^(k) and generate output 1518 that maycomprise the SCI values at intersection points.

FIG. 15B is a schematic diagram 1519 illustrating exemplary proceduresof a cooperative hybrid expert system for simulating SCI distribution onthe mesh-grid (or, equivalently, lattice grid) points of a structurefrom SCI distribution on the intersection points in accordance with oneembodiment of the present invention. For artificial damage such asattachment of the various-sized rubber patches on a structure with theprior-known information on the location and extent of the damage, theclassification module can generate output 1528 that may be the first SCIchromosomes s_(prior) ^(j) on the grid points following the steps1418-1426. If the input 1518 to this corporative hybrid expert system isthe SCI distribution on the intersection points given with thecoordinates of the rubber patches and their sizes, the final output 1540of this cooperative hybrid expert system may be the SCI distribution for‘hot-spot’ regions for the various sized rubber patches by using anadapted SCI chromosome set 1524, which is derived form the steps 1534,1536 and 1538. Moreover, the neuro-fuzzy inference system as shown inFIG. 15A may be applied again to the intersection points and their SCIvalues 1518 for the artificial damage, and adapted SCI chromosomess_(adapt) ^(j) 1524 may be obtained by the use of steps 1418-1426 fromoutput y_(adapt) ^(k) of the neuro-fuzzy inference system shown in FIG.15A. In step 1534, the difference between the two chromosome set may becalculated to give a root mean square norm E: E=√{square root over((s_(prior) ^(j)−s_(adopt) ^(j))²)}, j=1, . . . , n×m, where n×m is thedimension of the grid points. The fitness value of each chromosome isdetermined in step 1536 according to the calculated difference:fitness=exp(−E). Then, the genetic operation in step 1538 may beperformed for the crossover and mutation of chromosomes, where theoperation scheme in this module may use genetic algorithms in the art.Then, the classification module may provide the SCI chromosomedistribution 1524 on the grid points, best fitted to the artificialdamage. With these SCI chromosomes, an unsupervised neural network canbe trained in step 1526 to achieve the clustering or classification onthe SCI distribution set on the grid points. However, the classificationmodule can repeat to adapt the hybrid expert system while the processmodule process to renew the SCI distributions for each excitationfrequency.

The classification module may continue to classify the damage types (or,equivalently, ‘hot-spot’ regions) from the SCI distribution 1540 on thegrid points. FIG. 16A is a schematic diagram 1600 illustrating Gaborjets applied to a ‘hot-spot’ region in accordance with one embodiment ofthe present teachings. As illustrated in FIG. 16, a ‘hot-spot’ region1610 may be recognized and segmented from the background SCIdistribution 1602 on the grid points. In general, the shape and locationof the hot-spot region 1610 may vary according to the excitationfrequency and the number of network paths. Also, the diversity inphysical characteristic and geometry of structures monitored mayincrease the difficulty level in classifying the damages. In oneembodiment of the present invention, the classification module mayemploy a multilayer perception (MLP) or feedforward neural network toclassify the damage of ‘hot-spot’ region 1610 in a structure. Theclassification module may use Gabor wavelet features 1606 to combinethose features into a MLP as will be explained later. The Gabor waveletfeatures 1606 may be obtained from the Gabor wavelet transformation ofthe SCI distribution with different orientations 1608 andmultiresolution scales 1604. The Gabor wavelet function may be definedasG(x,y)=exp{−λ[(x−x ₀)²α²+(y−y ₀)²β²]+2πj[u ₀(x−x ₀)+v ₀(y−y ₀)]}, wherej=√{square root over (−1)}.(x₀, y₀) are position parameters to localize the wavelet to a selectedregion, (u₀, v₀) are modulation parameters to orient the wavelet in apreferred direction, and (α, β) are scaled parameters. With a set ofcoefficient called ‘Gabor jet’, the classification module may computethe Gabor project for multiple orientations and resolutions at a given‘hot-spot’ region 1610. Each Gabor jet may contain a number ofcoefficients corresponding to the number of orientations and theresolution levels such that it consists of logons of orientations anddifferent scales. The classification module can capture localSCI-distribution structure of each of the ‘hot-spot’ regions bycomputing a set of Gabor jets at several points of the region to get theinput feature.

FIG. 16B is a schematic diagram 1620 illustrating multilayer perception(MLP) for classifying the type of damage in accordance with oneembodiment of the present teachings. As illustrated in FIG. 16B, the MLP1624 may include three layers: an input feature layer 1628 for receivingGabor jets; a hidden layer 1630; and a output classification layer 1632for determining the types of damages in hot-spots 1610. A number ofneurons in the output classification layer 1632 can be the nodesrepresenting the structural condition types.

FIG. 16C is a schematic diagram 1640 illustrating the fully connectednetwork classifier for classifying a structural condition in accordancewith one embodiment of the present teachings. As illustrated in FIG.16C, a set of Gabor jets 1642 may be generated using a SCI distribution1643 that may contain 3 hot-spot regions 1641. A MLP 1644 may be similarto the MPL 1624 and classify the types of damages in hot-spot regions1641 into one of the categories C0-C5 1646. For simplicity, only threehot-spots regions 1641 and six categories are shown in FIG. 16C.However, it should be apparent to those of ordinary skill that thepresent invention may be practiced with any number of hot-spot regionsand categories.

FIG. 16D is a schematic diagram 1650 illustrating modular networkclassifiers for classifying structural conditions in accordance with oneembodiment of the present teachings. As illustrated in FIG. 16D, a setof Gabor jets 1652 for each hot-spot region 1641 of the SCI distribution1643 may be generated. Each MLP 1654 may be similar to the MPL 1624 andclassify the type of damage in each hot-spot region 1641. Then, anonlinear transformation and mixing process 1655 may be applied to theresults from the MLP 1654 prior to the classification of the damages.The structural condition may be trained with the different condition ordamage of structures so that the highest value in the output nodes maybe taken to be one of the structural condition types.

For each type of the structural condition or damage, the diagnosisclassification module may setup reference templates as a “codebook” inaccordance with one embodiment of the present teachings. The codebookfor each type of damage may be the data set of cluster points of thedifferent versions of SCI distribution or of wavelet transformationcoefficients of the SCI distribution, explained later in FIG. 17B. Eachtemplate or SCI distribution for the ‘hot-spot’ region may be clusteredby a K-mean and learning vector quantization (LVQ) clustering algorithm.The K-mean algorithm may partition a collection of n vector into cgroups G_(i), i=1, . . . , c and finds a cluster center in each groupsuch that a cost function of dissimilarity measure may be minimized.This algorithm may use an unsupervised learning data clustering methodto locate several clusters without using the class information. Once theK-mean algorithm determines the clusters of SCI distribution of the‘hot-spot’ region on the grid points, the clustered data may be labeledbefore moving to the second step of a supervised learning to locateseveral cluster centers. During the supervised learning, the clustercenters may be fine-tuned to approximate a desired decisionhypersurface. The learning method may be straightforward. First, thecluster center c that is closest to the input vector x must be found.Then, if x and c belong to the same class, c is moved toward x;otherwise c is moved form the input vector x. This LVQ algorithm canclassify an input vector by assigning it to the same class as the outputunit that has the weight vector closest to the input vector. Thus, theLVQ network may use the class information of SCI values to fine-tune thecluster centers to minimize the number of misclassified cases.

FIG. 17A is a flow chart 1700 illustrating exemplary procedures of aK-mean/LVQ algorithm for developing a clustered ‘codebook’ in accordancewith one embodiment of the present teachings. The classification modulemay begin the first K-mean clustering process, as an unsupervisedlearning data clustering method, with step 1702 where the clustercenters c_(i), i=1, . . . , c may be initialized by randomly selecting cpoints from the SCI data on the ‘hot-spot’ regions. In step 1704, theclassification module may determine the membership matrix S by theequation: s_(ik)=if ∥x_(k)−c_(i)∥≦∥x_(k)−c_(i)∥; 0 otherwise, where thebinary membership matrix S may define the c partition groups of G_(i),i=1, . . . , c, and x is a randomly selected input vector. Then, theclassification module may compute in step 1706 the cost function of

$L = {{\sum\limits_{i = 1}^{c}{L_{i}\mspace{14mu}{and}\mspace{14mu} L_{i}}} = {\sum\limits_{x_{k} \in G_{i}}{{x_{k} - c_{i}}}^{2}}}$where the Euclidean distance may be chosen as the dissimilarity measurebetween the SCI vector x_(k) and the corresponding cluster center c_(i).Next, in step 1708, the cluster centers may be updated according to theequation

$c_{i} = {{1/{G_{i}}}{\sum\limits_{x_{k} \in G_{i}}x_{k}}}$and go to decision step 1710 to check if either the cost is below acertain tolerance value. If answer to the step 1710 is YES, the processproceeds to the step 1714. Otherwise, it may proceed to another decisionstep 1712 to determine if the newly calculated cost is smaller than theprevious one. If answer to the step 1712 is NO, the process proceeds tothe step 1714. Otherwise, it may proceed to step 1704. Next, theclassification module may begin the second LVQ clustering process tofine-tune the cluster centers in step 1714 to minimize the number ofmisclassified cases. Here, the clusters obtained from the steps1702-1708 may be labeled by a voting method (i.e., a cluster is labeledclass i if it has data points belong to class i as a majority within thecluster.) In step 1716, the classification module may randomly select atraining input vector x and find i such that ∥x_(k)−c_(i)∥ is a minimum.Next, in step 1718, the classification module may update c_(i) byc_(i)=γ(x_(k)−c_(i)) if x_(k) and c_(i) belong to the same class;otherwise by c_(i)=−γ(x_(k)−c_(i)), where γ is a learning rate and apositive small constant that may decrease with each of iterations. Instep 1720, the classification module can generate a codebook that mayinclude the SCI cluster center of the SCI distribution of the ‘hot-spot’regions on the grid points.

FIG. 17B is a schematic diagram 1730 illustrating exemplary proceduresof a classification module to build a damage classifier using a codebookgenerated by the steps in FIG. 17A in accordance with one embodiment ofthe present teachings. The damages may be located in a ‘hot-spot’ regionon the grid points of the diagnostic network paths. The SCI distribution1734 of ‘hot-spot’ regions for each structural condition may be used todesign the codevector for structural conditions or damages, where eachtype of damage may belong to one of the types 1732. Each SCIdistribution 1734 may be obtained at an actuation frequency. For thenetwork signals measured at a different excitation frequency, anotherblock template 1738 can be also attained from the collection 1734 on theSCI distributions of the ‘hot-spot’ regions. The codevector may be givenby the set of the cluster centers of the block template of the SCIdistribution of the ‘hot-spot’ regions. Then, the classificationcodebook 1738 comprising a set of the optimized block templatesaccording to each of the structural condition or damage references maybe obtained by differentiating actuation frequency. In order toestablish the codebook-based classifiers considering the actuationfrequency, a frequency multilayer perception 1740 must be given in thecodevectors of the codebook 1738 corresponding to the set of actuationfrequencies. The output from the frequency multilayer perception 1740may be input into a neural network input layer 1741. Then, using theoutput from the neural network input layer 1741, other multilayerperception 1742 may also classify the structural condition or damage1744 to combine the outputs of the frequency multilayer perception. Inone embodiment of the present invention, the coefficients of Fourier andwavelet transformation of these SCI values instead of the SCI values ofthe ‘hot-spot’ regions can be utilized as the input of the K-meanalgorithm in FIG. 17A. In another embodiment of the present invention,the principal component analysis, incorporated with Fisher lineardiscriminant analysis or eigenspace separation transformation, can beused in the PCA-based LVQ clustering method for the SCI distributions orwavelet-transformed SCI distributions to provide different codebookswith high sensitivity to damage types.

A structure suffers aging, damage, wear and degradation in terms of itsoperation/service capabilities and reliability. So, it needs a holisticview that the structural life has different stages starting with theelaboration of need right up to the phase-out. Given a network patchsystem, the current wave transmission of the network patch system mayobey different time scales during the damage evolution to query thestructure of its time-variant structural properties. FIG. 18Aillustrates a schematic diagram 1800 of three evolution domains of astructure in operation/service, dynamics of the network patch system,and network system matrix in accordance with one embodiment of thepresent teachings. In illustrated in FIG. 18A, a slow-time coordinate τdesignating the structure damage evolution is introduced, and, inaddition, the fast-time coordinate n describing the current networkdynamics for the wave transmission is introduced.

In the fast timeframe nested in the long-term lifetime, the dynamicsystem of the diagnostic network patch system, as a black-box model tobe identified from the input actuation and output sensing signals, canbe described by an autoregressive moving average with exogenous inputs(ARMAX) or state space model. Rather than using the ARMAX model possiblyincorporated in a fault diagnostic system to query the functionality ofbuilt-in system components, the state-space dynamics models of thenetwork patch system at a fixed lifetime τ can be used. The state-spacedynamic model, considered in non-distributed domain for the brevity ofexplanation, may be represented by x_(τ)(n+1)=A_(τ)T_(τ)(n)+B_(τ)f(n),where the state vector x_(τ)(n) is the wave-transmission state vector ofthe network system and f (n) is the input force vector of the actuatorsin the network patches. A_(τ), B_(τ) are the system matrix and the inputmatrix, respectively. The excitation force for generating Lamb wave inall network paths is assumed to be unchanged during the lifetime ofτ_(e). The measurement equation of the network sensors is written asy_(τ)(n)=C_(τ)x_(τ)(n) where y_(τ)(n) is the sensor signal vector andC_(τ) is the system observation matrix. The system matrix Σ_(τ)(=[A_(τ),B_(τ), C_(τ)]) of the diagnostic network patch system can be consideredindependent of the fast time coordinate.

To model the network dynamics of the diagnostic patch system, theprognosis module may compute the system matrix τ_(r)(=[A_(τ), B_(τ),C_(τ)]) by using a subspace system identification method thatreconstructs the dynamic system from the measured actuator/sensorsignals in the network patches. The procedures disclosed by Kim et al.,“Estimation of normal mode and other system parameters of compositelaminated plates,” Composite Structures, 2001 and by Kim et al.,“Structural dynamic system reconstruction method for vibratingstructures, Transaction of DSMC, ASME, 2003, which are incorporatedherein in its entirety by reference thereto, can be employed toestablish the reconstructed dynamic system model using the multipleinputs and outputs of the present sensory network system.

A fundamental quantity for monitoring and diagnosis may be a symptomcontained in sensor signals measured from a time-variant system. Thestructural condition change or damage of a structure may essentiallyindicate the modification in wave transmission or dynamiccharacteristics of the structure system containing the network of aplurality of sensors and actuators a structure. The system matrix Σ_(τ)is observable and sensitive with respect to the structural conditionchange so that it can be considered as a symptom. The system matrix as asymptom can be applied one of suitable damage-related dynamiccharacteristics properties, for example, which may be naturalfrequencies, damping ratios and vibrational mode shapes to representstructural condition change as sensitive quantities fordamage/impact/aging of a structure. Thus, the structural condition indexI(τ) on the diagnostic network paths can be described by a nonlinearfunction with the variable of the system matrix Σ_(τ) in the lifetime:I(τ)=f(Σ_(τ)). Examples of similar approach can be found in “Damageidentification using reconstructed residual frequency responsefunctions”, Journal of Sound and Vibration, 2003, by Kim and “Bendingrigidity and natural frequency of debonded honeycomb sandwich beams”,Composite Structures, 2002, by Kim et al. and “Natural frequencyreduction model for matrix-dominated fatigue damage in compositelaminates”, Composite Structures, 2003, by Moon et al., which areincorporated herein, in its entirety, by reference thereto.

To determine the near future structural condition in damage evolutiondomain, the prognosis module may employ the current trend of the systemmatrix as the damage/impact related temporal symptom of a hoststructure. If the temporal symptom shows sign of deterioration, asexemplified by the change of damage/impact related symptom increasingwith time r, the prognostic module will predict the behavior of the‘hot-spot’ regions with respect to the remaining life span of astructure and trigger an early warning. Consequently, the future trendof the system matrix Σ_(τ) produced by the network dynamics of Lamb-wavetransmission on the structure makes it possible to forecast thestructure damage/impact conditions. To estimate the future system matrixΣ_(τ), the prognosis module preferably utilizes a training method ofrecurrent neural network (RNN) with the previous dynamic reconstructionmodels determined from the simulated sensor signals, because of itshighly nonlinear characteristics of the SCI vector I(τ). In analternative embodiment, the feed-forward neural network (FFN) can beused. The curves 1802 and 1810 may represent the evolution of the SCIvector I(τ) and the matrix Σ_(τ), respectively, and span up to the timeof structural death τ_(e) 1804. Sensor signals 1808 may be measured toaccess the structural conditions at time τ_(v) 1806.

FIG. 18B schematically illustrates the architecture of a recurrentneural network 1830 for forecasting the future system matrix inaccordance with one embodiment of the present teachings. As shown inFIG. 18B, the architecture of the RNN 1830 may have four input nodes1836 and additional feedback-path node 1838, four hidden nodes 1834 andone output node 1832. The input data set may be a set of the elements ofdiscrete time-delayed system matrix series. The output layer may consistof one neuron 1832 corresponding to the system matrix elements that arebeing predicted at the first time step in the future. In the RNN 1830,the current activation state of the output is a function of the previousactivation states as well as the current inputs. At time τ, the outputnode (output signal at τ+1) may be calculated by the activation ofhidden nodes 1834 at the previous time steps τ, τ−1, τ−2, . . . , τ−netc. Therefore, each training pattern will contain the current Σ_(τ),the previous three time lagged values {Σ_(τ−3), Σ_(τ−2), Σ_(τ−1)}, andan extra input from additional feedback loop 1840, and the output{circumflex over (Σ)}_(τ+1) is one step ahead predicted value. Thisnetwork can provide the estimated value of the next future system matrixbased on the current and previous system matrix values. A sigmoidfunction of 1/(1+e^(−x)) may be used as the activation functions of thenodes contained in the hidden and output layers. The nodes shouldoperate in the ranges of the activation functions, and all the elementdata in the system matrix in activation may be scaled to the interval[−0.5 0.5]. The level of the RNN's learning may be determined by aprediction error between the actual outputs from the network and thetarget outputs corresponding to an input data set. The error may beutilized in adjusting the weights until the actual outputs areconsistent with the target values. The RNN in prognosis module maycomplete the learning process when the number of training iterations hasreached a prescribed number and the error can be judged acceptablysmall.

By the use of the state-space model of the future system matrix{circumflex over (Σ)}_(τ+1), the prognosis module may develop theprognostic sensor signals for the ‘hot-spot’ regions of the structurefrom the inputs of the same actuator signals. Now, the identificationand classification methods, as explained in FIGS. 9-18B, can apply tothe prognostic sensor signals to compute the one-step ahead SCI vectorI(τ+1). Finally, the prognosis module can display the prognostictomography image and store it into a prognosis tomography databasedepository.

As mentioned, the monitoring software may comprise interrogation,processing, classification and prognosis modules. These applicationmodules may use extensible Markup Language (XML) to save their processeddata and/or images to a structured-query-language (SQL) based databaseand retrieve the reference and system data for device locations, networkpaths and parameters of structural condition monitoring system. EachXML-formatted document may be described by its data and tags created bythe structural monitoring system. Also, each module can parse the XMLdocument to read data that may be input to other application modules.Tags in XML documents may consist of root element in the outmost nodeand child elements in the nested nodes and may have attributes thatappear as name/value pairs following the name of the tag.

The structural health monitoring software can also have Simple ObjectAccess Protocol (SOAP) or RPC (Remote Procedure Call)-XML, which arelightweight protocol for exchanging SCI data and images in a distributedstructure computing system for structural condition monitoring. In thedistributed server system, all application modules can also be XML webservices capable of communicating and remotely computing across networkusing the open standard SOAP or XML-RPC with XML-formatted documents ofstructural condition information for all marshaled structure systems. Toprovide the XML web services for structural health monitoring, theapplication modules are abstracted as an entity by compiling them withCommon Object Module (COM), and then wrapped by applying a SOAP wrapper,such as SOAP Toolkit™ software from Microsoft. The application modulescan use a low-level Application Programming Interface (API) for directcontrol over the SOAP process for their COM objects.

While the present invention has been described with reference to thespecific embodiments thereof, it should be understood that the foregoingrelates to preferred embodiments of the invention and that modificationsmay be made without departing from the spirit and scope of the inventionas set forth in the following claims.

1. A computer-implemented method for interrogating health conditions ofa structure using a plurality of diagnostic network patches (DNP)implemented thereto, each of the patches being able to operate as atleast one of a transmitter patch and a sensor patch, the methodcomprising: forming a diagnostic network including the patches and aplurality of signal transmission paths, each said signal transmissionpath being a signal link between a transmitter patch and a sensor patch;representing the diagnostic network by a graph, wherein the patches andsignal transmission paths are respectively abstracted as nodes and edgesin the graph; partitioning the diagnostic network into one or moresubgroups, each of the subgroups including a designated transmitterpatch and one or more sensor patches; causing, by use of a computerprocessor, the designated transmitter patch to transmit a signal and thesensor patches to receive the signal; comparing the received signal witha baseline signal to determine a deviation therebetween, the baselinesignal being measured by use of the diagnostic network in absence ofstructural anomaly; and analyzing the deviation to determine the healthconditions of the structure.
 2. The method of claim 1, furthercomprising: storing the received signal and the deviation in adepository.
 3. The method of claim 1, wherein the step of analyzingincludes: performing diagnostic data processing; generating a structuralcondition index; and generating a tomographic image.
 4. The method ofclaim 1, wherein the anomaly includes at least one selected from thegroup consisting of damage, impact, cavity, corrosion, local change ofinternal temperature and pressure, degradation of material, and a repairbonding patch applied to the structure.
 5. The method of claim 1,wherein two neighboring subgroups share one or more patches.
 6. Themethod claim 1, wherein the step of representing the diagnostic networkby a graph includes: generating a topological architecture, x, of thediagnostic network, wherein x is represented by the equation:x={x₁₂, x₁₃, . . . , x_(n−1, n)} and wherein x_(ij) ε{0,1} is a decisionvariable representing the path between i^(th) and j^(th) nodes and n isthe number of the nodes.
 7. The method of claim 1, wherein the step ofrepresenting the diagnostic network by a graph includes: performing acommon integer programming formulation to generate a matrix of pathconnection, each element (i,k) of the matrix being 1 if i^(th) sensorpatch is connected to k^(th) transmitter patch and 0 otherwise.
 8. Themethod of claim 1, further comprising: optimizing the diagnostic networkby use of a cost variable associated with network path uniformity sothat network performance is maximized while the number of patches isminimized.
 9. The method of claim 8, wherein the cost variable is thedistance of signal transmission, the number of intersection points ofeach said signal transmission path crossed by neighboring paths or asensitivity of each said signal transmission path to an excitationfrequency of the signal.
 10. The method of claim 1, further comprising,prior to the step of forming a diagnostic network: applying one or moreartificial defects to the structure to simulate damages in thestructure, wherein the step of forming a diagnostic network is performedby a genetic algorithm to optimize the diagnostic network.
 11. Themethod of claim 1, wherein the step of causing the designatedtransmitter patch to transmit a signal and the sensor patches to receivethe signal includes: providing a toneburst signal for the designatedtransmitter patch, the toneburst signal having a spectral energydistribution within a narrow frequency bandwidth centered at oneexcitation frequency.
 12. The method of claim 1, further comprising:repeating the steps of forming a diagnostic network to comparing thereceived signal at a plurality of excitation frequencies of the signal.13. The method of claim 1, wherein the signal is a Lamb wave signal or avibrational signal.
 14. The method of claim 1, further comprising:storing coordinates of patches and setup information including anexcitation frequency of the signal, types of patches, identificationnumbers of patches, a voltage level of patches, and operational statusof patches.
 15. The method of claim 1, further comprising: convertingthe baseline signal and received signal into eXtensible Markup Language(XML) formatted data.
 16. A computer readable medium carrying one ormore sequences of instructions for interrogating health conditions of astructure using a plurality of diagnostic network patches (DNP)implemented thereto, each of the patches being able to operate as atleast one of a transmitter patch and a sensor patch, wherein executionof one or more sequences of instructions by one or more processorscauses the one or more processors to perform the steps of: forming adiagnostic network including the patches and a plurality of signaltransmission paths, each said signal transmission path being a signallink between a transmitter patch and a sensor patch; representing thediagnostic network by a graph, wherein the patches and signaltransmission paths are respectively abstracted as nodes and edges in thegraph; partitioning the diagnostic network into one or more subgroups,each of the subgroups including a designated transmitter patch and oneor more sensor patches; causing the designated transmitter patch totransmit a signal and the sensor patches to receive the signal;comparing the received signal with a baseline signal to determine adeviation therebetween, the baseline signal being measured by use of thediagnostic network in absence of structural anomaly; and analyzing thedeviation to determine the health conditions of the structure.
 17. Thecomputer readable medium of claim 16, wherein execution of one or moresequences of instructions by one or more processors causes the one ormore processors to perform the additional step of storing the receivedsignal and the deviation in a depository.
 18. The computer readablemedium of claim 16, wherein the step of analyzing includes: performingdiagnostic data processing; generating a structural condition index; andgenerating a tomographic image.
 19. The computer readable medium ofclaim 16, wherein two neighboring subgroups share one or more patches.20. The computer readable medium of claim 16, wherein the step ofrepresenting the diagnostic network by a graph includes: generating atopological architecture, x, of the diagnostic network, wherein x isrepresented by the equation:x={x₁₂, x₁₃, . . . , x_(n−1, n)} and wherein x_(ij) ε{0,1} is a decisionvariable representing the path between i^(th) and j^(th) nodes and n isthe number of the nodes.
 21. The computer readable medium of claim 16,wherein the step of representing the diagnostic network by a graphincludes: performing a common integer programming formulation togenerate a matrix of path connection, each element (i,k) of the matrixbeing 1 if i^(th) sensor patch is connected to k^(th) transmitter patchand 0 otherwise.
 22. The computer readable medium of claim 16, whereinexecution of one or more sequences of instructions by one or moreprocessors causes the one or more processors to perform the additionalstep of optimizing the diagnostic network by use of a cost variableassociated with network path uniformity so that network performance ismaximized while the number of patches is minimized.
 23. The computerreadable medium of claim 22, wherein the cost variable is the distanceof signal transmission, the number of intersection points of each saidsignal transmission path crossed by neighboring paths or a sensitivityof each said signal transmission path to an excitation frequency of thesignal.
 24. The computer readable medium of claim 16, wherein executionof one or more sequences of instructions by one or more processorscauses the one or more processors to perform the additional step of,prior to the step of forming a diagnostic network: applying one or moreartificial defects to the structure to simulate damages in thestructure, wherein the step of forming a diagnostic network is performedby a genetic algorithm to optimize the diagnostic network.
 25. Thecomputer readable medium of claim 16, wherein execution of one or moresequences of instructions by one or more processors causes the one ormore processors to perform the additional step of repeating the steps offorming a diagnostic network to comparing the received signal at aplurality of excitation frequencies of the signal.
 26. The computerreadable medium of claim 16, wherein execution of one or more sequencesof instructions by one or more processors causes the one or moreprocessors to perform the additional step of: storing coordinates ofpatches and setup information including an excitation frequency of thesignal, types of patches, identification numbers of patches, a voltagelevel of patches, and operational status of patches.
 27. The method ofclaim 16, wherein execution of one or more sequences of instructions byone or more processors causes the one or more processors to perform theadditional step of converting the baseline signal and received signalinto eXtensible Markup Language (XML) formatted data.
 28. The computerreadable medium of claim 16, wherein the one or more sequences ofinstructions implement a remote processing method of Simple ObjectAccess Protocol (SOAP) or Remote Procedure Call/eXtensible MarkupLanguage (RPC-XML) for Internet Web Services.